import numpy as np # library to handle data in a vectorized manner
import pandas as pd # library for data analysis
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
import json # library to handle JSON files
!pip install geopy
from geopy.geocoders import Nominatim # convert an address into latitude and longitude values
import requests # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
# Visualization modules
import matplotlib.cm as cm
import matplotlib.colors as colors
# import k-means from clustering stage
from sklearn.cluster import KMeans
!pip install folium
import folium # map rendering library
print('Libraries imported.')
Requirement already satisfied: geopy in c:\users\welcome\anaconda3\lib\site-packages (1.20.0) Requirement already satisfied: geographiclib<2,>=1.49 in c:\users\welcome\anaconda3\lib\site-packages (from geopy) (1.49) Requirement already satisfied: folium in c:\users\welcome\anaconda3\lib\site-packages (0.10.0) Requirement already satisfied: requests in c:\users\welcome\anaconda3\lib\site-packages (from folium) (2.0.0) Requirement already satisfied: numpy in c:\users\welcome\anaconda3\lib\site-packages (from folium) (1.16.3) Requirement already satisfied: jinja2>=2.9 in c:\users\welcome\anaconda3\lib\site-packages (from folium) (2.10.1) Requirement already satisfied: branca>=0.3.0 in c:\users\welcome\anaconda3\lib\site-packages (from folium) (0.3.1) Requirement already satisfied: MarkupSafe>=0.23 in c:\users\welcome\anaconda3\lib\site-packages (from jinja2>=2.9->folium) (1.1.1) Requirement already satisfied: six in c:\users\welcome\anaconda3\lib\site-packages (from branca>=0.3.0->folium) (1.12.0) Libraries imported.
with open('new_york_data.json') as json_data:
newyork_data = json.load(json_data)
newyork_data
{'type': 'FeatureCollection',
'totalFeatures': 306,
'features': [{'type': 'Feature',
'id': 'nyu_2451_34572.1',
'geometry': {'type': 'Point',
'coordinates': [-73.84720052054902, 40.89470517661]},
'geometry_name': 'geom',
'properties': {'name': 'Wakefield',
'stacked': 1,
'annoline1': 'Wakefield',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.84720052054902,
40.89470517661,
-73.84720052054902,
40.89470517661]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.2',
'geometry': {'type': 'Point',
'coordinates': [-73.82993910812398, 40.87429419303012]},
'geometry_name': 'geom',
'properties': {'name': 'Co-op City',
'stacked': 2,
'annoline1': 'Co-op',
'annoline2': 'City',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.82993910812398,
40.87429419303012,
-73.82993910812398,
40.87429419303012]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.3',
'geometry': {'type': 'Point',
'coordinates': [-73.82780644716412, 40.887555677350775]},
'geometry_name': 'geom',
'properties': {'name': 'Eastchester',
'stacked': 1,
'annoline1': 'Eastchester',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.82780644716412,
40.887555677350775,
-73.82780644716412,
40.887555677350775]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.4',
'geometry': {'type': 'Point',
'coordinates': [-73.90564259591682, 40.89543742690383]},
'geometry_name': 'geom',
'properties': {'name': 'Fieldston',
'stacked': 1,
'annoline1': 'Fieldston',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90564259591682,
40.89543742690383,
-73.90564259591682,
40.89543742690383]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.5',
'geometry': {'type': 'Point',
'coordinates': [-73.9125854610857, 40.890834493891305]},
'geometry_name': 'geom',
'properties': {'name': 'Riverdale',
'stacked': 1,
'annoline1': 'Riverdale',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.9125854610857,
40.890834493891305,
-73.9125854610857,
40.890834493891305]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.6',
'geometry': {'type': 'Point',
'coordinates': [-73.90281798724604, 40.88168737120521]},
'geometry_name': 'geom',
'properties': {'name': 'Kingsbridge',
'stacked': 1,
'annoline1': 'Kingsbridge',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90281798724604,
40.88168737120521,
-73.90281798724604,
40.88168737120521]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.7',
'geometry': {'type': 'Point',
'coordinates': [-73.91065965862981, 40.87655077879964]},
'geometry_name': 'geom',
'properties': {'name': 'Marble Hill',
'stacked': 2,
'annoline1': 'Marble',
'annoline2': 'Hill',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Manhattan',
'bbox': [-73.91065965862981,
40.87655077879964,
-73.91065965862981,
40.87655077879964]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.8',
'geometry': {'type': 'Point',
'coordinates': [-73.86731496814176, 40.89827261213805]},
'geometry_name': 'geom',
'properties': {'name': 'Woodlawn',
'stacked': 1,
'annoline1': 'Woodlawn',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.86731496814176,
40.89827261213805,
-73.86731496814176,
40.89827261213805]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.9',
'geometry': {'type': 'Point',
'coordinates': [-73.8793907395681, 40.87722415599446]},
'geometry_name': 'geom',
'properties': {'name': 'Norwood',
'stacked': 1,
'annoline1': 'Norwood',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8793907395681,
40.87722415599446,
-73.8793907395681,
40.87722415599446]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.10',
'geometry': {'type': 'Point',
'coordinates': [-73.85744642974207, 40.88103887819211]},
'geometry_name': 'geom',
'properties': {'name': 'Williamsbridge',
'stacked': 1,
'annoline1': 'Williamsbridge',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85744642974207,
40.88103887819211,
-73.85744642974207,
40.88103887819211]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.11',
'geometry': {'type': 'Point',
'coordinates': [-73.83579759808117, 40.866858107252696]},
'geometry_name': 'geom',
'properties': {'name': 'Baychester',
'stacked': 1,
'annoline1': 'Baychester',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.83579759808117,
40.866858107252696,
-73.83579759808117,
40.866858107252696]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.12',
'geometry': {'type': 'Point',
'coordinates': [-73.85475564017999, 40.85741349808865]},
'geometry_name': 'geom',
'properties': {'name': 'Pelham Parkway',
'stacked': 1,
'annoline1': 'Pelham Parkway',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85475564017999,
40.85741349808865,
-73.85475564017999,
40.85741349808865]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.13',
'geometry': {'type': 'Point',
'coordinates': [-73.78648845267413, 40.84724670491813]},
'geometry_name': 'geom',
'properties': {'name': 'City Island',
'stacked': 2,
'annoline1': 'City',
'annoline2': 'Island',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.78648845267413,
40.84724670491813,
-73.78648845267413,
40.84724670491813]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.14',
'geometry': {'type': 'Point',
'coordinates': [-73.8855121841913, 40.870185164975325]},
'geometry_name': 'geom',
'properties': {'name': 'Bedford Park',
'stacked': 2,
'annoline1': 'Bedford',
'annoline2': 'Park',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8855121841913,
40.870185164975325,
-73.8855121841913,
40.870185164975325]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.15',
'geometry': {'type': 'Point',
'coordinates': [-73.9104159619131, 40.85572707719664]},
'geometry_name': 'geom',
'properties': {'name': 'University Heights',
'stacked': 2,
'annoline1': 'University',
'annoline2': 'Heights',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.9104159619131,
40.85572707719664,
-73.9104159619131,
40.85572707719664]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.16',
'geometry': {'type': 'Point',
'coordinates': [-73.91967159119565, 40.84789792606271]},
'geometry_name': 'geom',
'properties': {'name': 'Morris Heights',
'stacked': 2,
'annoline1': 'Morris',
'annoline2': 'Heights',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.91967159119565,
40.84789792606271,
-73.91967159119565,
40.84789792606271]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.17',
'geometry': {'type': 'Point',
'coordinates': [-73.89642655981623, 40.86099679638654]},
'geometry_name': 'geom',
'properties': {'name': 'Fordham',
'stacked': 1,
'annoline1': 'Fordham',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.89642655981623,
40.86099679638654,
-73.89642655981623,
40.86099679638654]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.18',
'geometry': {'type': 'Point',
'coordinates': [-73.88735617532338, 40.84269615786053]},
'geometry_name': 'geom',
'properties': {'name': 'East Tremont',
'stacked': 2,
'annoline1': 'East',
'annoline2': 'Tremont',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.88735617532338,
40.84269615786053,
-73.88735617532338,
40.84269615786053]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.19',
'geometry': {'type': 'Point',
'coordinates': [-73.87774474910545, 40.83947505672653]},
'geometry_name': 'geom',
'properties': {'name': 'West Farms',
'stacked': 2,
'annoline1': 'West',
'annoline2': 'Farms',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.87774474910545,
40.83947505672653,
-73.87774474910545,
40.83947505672653]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.20',
'geometry': {'type': 'Point',
'coordinates': [-73.9261020935813, 40.836623010706056]},
'geometry_name': 'geom',
'properties': {'name': 'High Bridge',
'stacked': 1,
'annoline1': 'Highbridge',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.9261020935813,
40.836623010706056,
-73.9261020935813,
40.836623010706056]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.21',
'geometry': {'type': 'Point',
'coordinates': [-73.90942160757436, 40.819754370594936]},
'geometry_name': 'geom',
'properties': {'name': 'Melrose',
'stacked': 1,
'annoline1': 'Melrose',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90942160757436,
40.819754370594936,
-73.90942160757436,
40.819754370594936]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.22',
'geometry': {'type': 'Point',
'coordinates': [-73.91609987487575, 40.80623874935177]},
'geometry_name': 'geom',
'properties': {'name': 'Mott Haven',
'stacked': 1,
'annoline1': 'Mott Haven',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.91609987487575,
40.80623874935177,
-73.91609987487575,
40.80623874935177]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.23',
'geometry': {'type': 'Point',
'coordinates': [-73.91322139386135, 40.801663627756206]},
'geometry_name': 'geom',
'properties': {'name': 'Port Morris',
'stacked': 2,
'annoline1': 'Port',
'annoline2': 'Morris',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.91322139386135,
40.801663627756206,
-73.91322139386135,
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{'type': 'Feature',
'id': 'nyu_2451_34572.24',
'geometry': {'type': 'Point',
'coordinates': [-73.8957882009446, 40.81509904545822]},
'geometry_name': 'geom',
'properties': {'name': 'Longwood',
'stacked': 1,
'annoline1': 'Longwood',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8957882009446,
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{'type': 'Feature',
'id': 'nyu_2451_34572.25',
'geometry': {'type': 'Point',
'coordinates': [-73.88331505955291, 40.80972987938709]},
'geometry_name': 'geom',
'properties': {'name': 'Hunts Point',
'stacked': 2,
'annoline1': 'Hunts',
'annoline2': 'Point',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.88331505955291,
40.80972987938709,
-73.88331505955291,
40.80972987938709]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.26',
'geometry': {'type': 'Point',
'coordinates': [-73.90150648943059, 40.82359198585534]},
'geometry_name': 'geom',
'properties': {'name': 'Morrisania',
'stacked': 1,
'annoline1': 'Morrisania',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90150648943059,
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-73.90150648943059,
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{'type': 'Feature',
'id': 'nyu_2451_34572.27',
'geometry': {'type': 'Point',
'coordinates': [-73.86574609554924, 40.821012197914015]},
'geometry_name': 'geom',
'properties': {'name': 'Soundview',
'stacked': 1,
'annoline1': 'Soundview',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.86574609554924,
40.821012197914015,
-73.86574609554924,
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{'type': 'Feature',
'id': 'nyu_2451_34572.28',
'geometry': {'type': 'Point',
'coordinates': [-73.85414416189266, 40.80655112003589]},
'geometry_name': 'geom',
'properties': {'name': 'Clason Point',
'stacked': 2,
'annoline1': 'Clason',
'annoline2': 'Point',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85414416189266,
40.80655112003589,
-73.85414416189266,
40.80655112003589]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.29',
'geometry': {'type': 'Point',
'coordinates': [-73.81635002158441, 40.81510925804005]},
'geometry_name': 'geom',
'properties': {'name': 'Throgs Neck',
'stacked': 1,
'annoline1': 'Throgs Neck',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.81635002158441,
40.81510925804005,
-73.81635002158441,
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{'type': 'Feature',
'id': 'nyu_2451_34572.30',
'geometry': {'type': 'Point',
'coordinates': [-73.8240992675385, 40.844245936947374]},
'geometry_name': 'geom',
'properties': {'name': 'Country Club',
'stacked': 2,
'annoline1': 'Country',
'annoline2': 'Club',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8240992675385,
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-73.8240992675385,
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{'type': 'Feature',
'id': 'nyu_2451_34572.31',
'geometry': {'type': 'Point',
'coordinates': [-73.85600310535783, 40.837937822267286]},
'geometry_name': 'geom',
'properties': {'name': 'Parkchester',
'stacked': 1,
'annoline1': 'Parkchester',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85600310535783,
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-73.85600310535783,
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{'type': 'Feature',
'id': 'nyu_2451_34572.32',
'geometry': {'type': 'Point',
'coordinates': [-73.84219407604444, 40.8406194964327]},
'geometry_name': 'geom',
'properties': {'name': 'Westchester Square',
'stacked': 2,
'annoline1': 'Westchester',
'annoline2': 'Square',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.84219407604444,
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-73.84219407604444,
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{'type': 'Feature',
'id': 'nyu_2451_34572.33',
'geometry': {'type': 'Point',
'coordinates': [-73.8662991807561, 40.84360847124718]},
'geometry_name': 'geom',
'properties': {'name': 'Van Nest',
'stacked': 2,
'annoline1': 'Van',
'annoline2': 'Nest',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8662991807561,
40.84360847124718,
-73.8662991807561,
40.84360847124718]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.34',
'geometry': {'type': 'Point',
'coordinates': [-73.85040178030421, 40.847549063536334]},
'geometry_name': 'geom',
'properties': {'name': 'Morris Park',
'stacked': 1,
'annoline1': 'Morris Park',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85040178030421,
40.847549063536334,
-73.85040178030421,
40.847549063536334]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.35',
'geometry': {'type': 'Point',
'coordinates': [-73.88845196134804, 40.85727710073895]},
'geometry_name': 'geom',
'properties': {'name': 'Belmont',
'stacked': 1,
'annoline1': 'Belmont',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.88845196134804,
40.85727710073895,
-73.88845196134804,
40.85727710073895]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.36',
'geometry': {'type': 'Point',
'coordinates': [-73.91719048210393, 40.88139497727086]},
'geometry_name': 'geom',
'properties': {'name': 'Spuyten Duyvil',
'stacked': 2,
'annoline1': 'Spuyten',
'annoline2': 'Duyvil',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.91719048210393,
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-73.91719048210393,
40.88139497727086]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.37',
'geometry': {'type': 'Point',
'coordinates': [-73.90453054908927, 40.90854282950666]},
'geometry_name': 'geom',
'properties': {'name': 'North Riverdale',
'stacked': 2,
'annoline1': 'North',
'annoline2': 'Riverdale',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90453054908927,
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-73.90453054908927,
40.90854282950666]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.38',
'geometry': {'type': 'Point',
'coordinates': [-73.8320737824047, 40.85064140940335]},
'geometry_name': 'geom',
'properties': {'name': 'Pelham Bay',
'stacked': 2,
'annoline1': 'Pelham',
'annoline2': 'Bay',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.8320737824047,
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-73.8320737824047,
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{'type': 'Feature',
'id': 'nyu_2451_34572.39',
'geometry': {'type': 'Point',
'coordinates': [-73.82620275994073, 40.82657951686922]},
'geometry_name': 'geom',
'properties': {'name': 'Schuylerville',
'stacked': 1,
'annoline1': 'Schuylerville',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
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'annoline3': None,
'annoangle': 0.0,
'borough': 'Brooklyn',
'bbox': [-73.95918459428702,
40.598525095137255,
-73.95918459428702,
40.598525095137255]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.291',
'geometry': {'type': 'Point',
'coordinates': [-73.88114319200604, 40.716414511158185]},
'geometry_name': 'geom',
'properties': {'name': 'Middle Village',
'stacked': 2,
'annoline1': 'Middle',
'annoline2': 'Village',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Queens',
'bbox': [-73.88114319200604,
40.716414511158185,
-73.88114319200604,
40.716414511158185]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.292',
'geometry': {'type': 'Point',
'coordinates': [-74.20152556457658, 40.52626406734812]},
'geometry_name': 'geom',
'properties': {'name': "Prince's Bay",
'stacked': 2,
'annoline1': "Prince's",
'annoline2': 'Bay',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Staten Island',
'bbox': [-74.20152556457658,
40.52626406734812,
-74.20152556457658,
40.52626406734812]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.293',
'geometry': {'type': 'Point',
'coordinates': [-74.13792663771568, 40.57650629379489]},
'geometry_name': 'geom',
'properties': {'name': 'Lighthouse Hill',
'stacked': 2,
'annoline1': 'Lighthouse',
'annoline2': 'Hill',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Staten Island',
'bbox': [-74.13792663771568,
40.57650629379489,
-74.13792663771568,
40.57650629379489]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.294',
'geometry': {'type': 'Point',
'coordinates': [-74.22957080626941, 40.51954145748909]},
'geometry_name': 'geom',
'properties': {'name': 'Richmond Valley',
'stacked': 2,
'annoline1': 'Richmond',
'annoline2': 'Valley',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Staten Island',
'bbox': [-74.22957080626941,
40.51954145748909,
-74.22957080626941,
40.51954145748909]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.295',
'geometry': {'type': 'Point',
'coordinates': [-73.82667757138641, 40.79060155670148]},
'geometry_name': 'geom',
'properties': {'name': 'Malba',
'stacked': 1,
'annoline1': 'Malba',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Queens',
'bbox': [-73.82667757138641,
40.79060155670148,
-73.82667757138641,
40.79060155670148]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.296',
'geometry': {'type': 'Point',
'coordinates': [-73.890345709872, 40.6819989345173]},
'geometry_name': 'geom',
'properties': {'name': 'Highland Park',
'stacked': 2,
'annoline1': 'Highland',
'annoline2': 'Park',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Brooklyn',
'bbox': [-73.890345709872,
40.6819989345173,
-73.890345709872,
40.6819989345173]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.297',
'geometry': {'type': 'Point',
'coordinates': [-73.94841515328893, 40.60937770113766]},
'geometry_name': 'geom',
'properties': {'name': 'Madison',
'stacked': 1,
'annoline1': 'Madison',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Brooklyn',
'bbox': [-73.94841515328893,
40.60937770113766,
-73.94841515328893,
40.60937770113766]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.298',
'geometry': {'type': 'Point',
'coordinates': [-73.86172577555115, 40.85272297633017]},
'geometry_name': 'geom',
'properties': {'name': 'Bronxdale',
'stacked': 1,
'annoline1': 'Bronxdale',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.86172577555115,
40.85272297633017,
-73.86172577555115,
40.85272297633017]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.299',
'geometry': {'type': 'Point',
'coordinates': [-73.85931863221647, 40.86578787802982]},
'geometry_name': 'geom',
'properties': {'name': 'Allerton',
'stacked': 1,
'annoline1': 'Allerton',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.85931863221647,
40.86578787802982,
-73.85931863221647,
40.86578787802982]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.300',
'geometry': {'type': 'Point',
'coordinates': [-73.90152264513144, 40.8703923914147]},
'geometry_name': 'geom',
'properties': {'name': 'Kingsbridge Heights',
'stacked': 2,
'annoline1': 'Kingsbridge',
'annoline2': 'Heights',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Bronx',
'bbox': [-73.90152264513144,
40.8703923914147,
-73.90152264513144,
40.8703923914147]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.301',
'geometry': {'type': 'Point',
'coordinates': [-73.94817709920184, 40.64692606658579]},
'geometry_name': 'geom',
'properties': {'name': 'Erasmus',
'stacked': 1,
'annoline1': 'Erasmus',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Brooklyn',
'bbox': [-73.94817709920184,
40.64692606658579,
-73.94817709920184,
40.64692606658579]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.302',
'geometry': {'type': 'Point',
'coordinates': [-74.00011136202637, 40.75665808227519]},
'geometry_name': 'geom',
'properties': {'name': 'Hudson Yards',
'stacked': 2,
'annoline1': 'Hudson',
'annoline2': 'Yards',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Manhattan',
'bbox': [-74.00011136202637,
40.75665808227519,
-74.00011136202637,
40.75665808227519]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.303',
'geometry': {'type': 'Point',
'coordinates': [-73.80553002968718, 40.58733774018741]},
'geometry_name': 'geom',
'properties': {'name': 'Hammels',
'stacked': 1,
'annoline1': 'Hammels',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Queens',
'bbox': [-73.80553002968718,
40.58733774018741,
-73.80553002968718,
40.58733774018741]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.304',
'geometry': {'type': 'Point',
'coordinates': [-73.76596781445627, 40.611321691283834]},
'geometry_name': 'geom',
'properties': {'name': 'Bayswater',
'stacked': 1,
'annoline1': 'Bayswater',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Queens',
'bbox': [-73.76596781445627,
40.611321691283834,
-73.76596781445627,
40.611321691283834]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.305',
'geometry': {'type': 'Point',
'coordinates': [-73.94563070334091, 40.756091297094706]},
'geometry_name': 'geom',
'properties': {'name': 'Queensbridge',
'stacked': 1,
'annoline1': 'Queensbridge',
'annoline2': None,
'annoline3': None,
'annoangle': 0.0,
'borough': 'Queens',
'bbox': [-73.94563070334091,
40.756091297094706,
-73.94563070334091,
40.756091297094706]}},
{'type': 'Feature',
'id': 'nyu_2451_34572.306',
'geometry': {'type': 'Point',
'coordinates': [-74.08173992211962, 40.61731079252983]},
'geometry_name': 'geom',
'properties': {'name': 'Fox Hills',
'stacked': 2,
'annoline1': 'Fox',
'annoline2': 'Hills',
'annoline3': None,
'annoangle': 0.0,
'borough': 'Staten Island',
'bbox': [-74.08173992211962,
40.61731079252983,
-74.08173992211962,
40.61731079252983]}}],
'crs': {'type': 'name', 'properties': {'name': 'urn:ogc:def:crs:EPSG::4326'}},
'bbox': [-74.2492599487305,
40.5033187866211,
-73.7061614990234,
40.9105606079102]}
The data we need from this json data file is
we will make a pandas dataframe with the 4 columns as listed above but before making the dataframe we will extract what we need.
newyork_df = pd.DataFrame(columns = ['Borough_name','Neighborhood','Latitude','Longitude'])
newyork_df
| Borough_name | Neighborhood | Latitude | Longitude |
|---|
newyork_data_clean = newyork_data['features']
for data in newyork_data_clean:
neighborhood = data['properties']['name']
borough = data['properties']['borough']
location = data['geometry']['coordinates']
longitude,latitude = location[0],location[1]
data = {"Borough_name":borough,"Neighborhood":neighborhood,"Latitude":latitude,"Longitude":longitude}
newyork_df = newyork_df.append(data,ignore_index=True)
newyork_df.head()
| Borough_name | Neighborhood | Latitude | Longitude | |
|---|---|---|---|---|
| 0 | Bronx | Wakefield | 40.894705 | -73.847201 |
| 1 | Bronx | Co-op City | 40.874294 | -73.829939 |
| 2 | Bronx | Eastchester | 40.887556 | -73.827806 |
| 3 | Bronx | Fieldston | 40.895437 | -73.905643 |
| 4 | Bronx | Riverdale | 40.890834 | -73.912585 |
newyork_df['Borough_name'].value_counts()
Queens 81 Brooklyn 70 Staten Island 63 Bronx 52 Manhattan 40 Name: Borough_name, dtype: int64
newyork_df.shape
(306, 4)
The dataframe consists of Five Boroughs and 306 Neighborhoods.
Use geopy library to get the latitude and longitude values of New York City.
address = 'New York City, NY'
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of New York City are {}, {}.'.format(latitude, longitude))
The geograpical coordinate of New York City are 40.7127281, -74.0060152.
# create map of New York using latitude and longitude values
map_newyork = folium.Map(location=[latitude, longitude], zoom_start=10)
# add markers to map
for lat, lng, borough, neighborhood in zip(newyork_df['Latitude'], newyork_df['Longitude'], newyork_df['Borough_name'], newyork_df['Neighborhood']):
label = '{}, {}'.format(neighborhood, borough)
label = folium.Popup(label, parse_html=True)
folium.CircleMarker(
[lat, lng],
radius=5,
popup=label,
color='blue',
fill=True,
fill_color='#3186cc',
fill_opacity=0.7,
parse_html=False).add_to(map_newyork)
map_newyork
manhattan_data = newyork_df[newyork_df['Borough_name'] == 'Manhattan'].reset_index(drop=True)
Let's get the geographical coordinates of Manhattan.
address = 'Manhattan, NY'
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Manhattan are {}, {}.'.format(latitude, longitude))
The geograpical coordinate of Manhattan are 40.7900869, -73.9598295.
# create map of Manhattan using latitude and longitude values
map_manhattan = folium.Map(location=[latitude, longitude], zoom_start=11)
# add markers to map
for lat, lng, label in zip(manhattan_data['Latitude'], manhattan_data['Longitude'], manhattan_data['Neighborhood']):
label = folium.Popup(label, parse_html=True)
folium.CircleMarker(
[lat, lng],
radius=5,
popup=label,
color='blue',
fill=True,
fill_color='#3186cc',
fill_opacity=0.7,
parse_html=False).add_to(map_manhattan)
map_manhattan
manhattan_data.loc[0, 'Neighborhood']
'Marble Hill'
Get the neighborhood's latitude and longitude values.
neighborhood_latitude = manhattan_data.loc[0, 'Latitude'] # neighborhood latitude value
neighborhood_longitude = manhattan_data.loc[0, 'Longitude'] # neighborhood longitude value
neighborhood_name = manhattan_data.loc[0, 'Neighborhood'] # neighborhood name
print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name,
neighborhood_latitude,
neighborhood_longitude))
Latitude and longitude values of Marble Hill are 40.87655077879964, -73.91065965862981.
Now, let's get the top 100 venues that are in Marble Hill within a radius of 1000 meters.
Define Foursquare Credentials and Version.
CLIENT_ID = 'I4D4A1JPPOZV3TKA0ZQ12JORYAAAMWRIK5D5K1THXH5LECKU' # your Foursquare ID
CLIENT_SECRET = '2MWZDT2HP0NODCSYOQHM3YDBMTJI3SUGGT3XE5GPHINMCZRC' # your Foursquare Secret
VERSION = '20190604'
LIMIT = 100 # limit of number of venues returned by Foursquare API
radius = 1000 # define radius
# create URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
neighborhood_latitude,
neighborhood_longitude,
radius,
LIMIT)
url # display URL
'https://api.foursquare.com/v2/venues/explore?&client_id=BI0VHR1QJSEIBDYHKBMBWWXCXNBTFZOCHFWQRQVSEYZYP3CY&client_secret=3RL3VDQV254P0IXMDNB3ARNFYIMIUQ21PQMPEK5D1JFHJBVD&v=20190604&ll=40.87655077879964,-73.91065965862981&radius=1000&limit=100'
results = requests.get(url).json()
results
{'meta': {'code': 200, 'requestId': '5d7fc6b86001bc00384df5e6'},
'response': {'suggestedFilters': {'header': 'Tap to show:',
'filters': [{'name': 'Open now', 'key': 'openNow'},
{'name': '$-$$$$', 'key': 'price'}]},
'headerLocation': 'Marble Hill',
'headerFullLocation': 'Marble Hill, New York',
'headerLocationGranularity': 'neighborhood',
'totalResults': 82,
'suggestedBounds': {'ne': {'lat': 40.88555078779965,
'lng': -73.89877901972629},
'sw': {'lat': 40.86755076979963, 'lng': -73.92254029753333}},
'groups': [{'type': 'Recommended Places',
'name': 'recommended',
'items': [{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4baf59e8f964a520a6f93be3',
'name': 'Bikram Yoga',
'location': {'address': '5500 Broadway',
'crossStreet': '230th Street',
'lat': 40.876843690797934,
'lng': -73.90620384419528,
'labeledLatLngs': [{'label': 'display',
'lat': 40.876843690797934,
'lng': -73.90620384419528}],
'distance': 376,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5500 Broadway (230th Street)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d102941735',
'name': 'Yoga Studio',
'pluralName': 'Yoga Studios',
'shortName': 'Yoga Studio',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/gym_yogastudio_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4baf59e8f964a520a6f93be3-0'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4b4429abf964a52037f225e3',
'name': "Arturo's",
'location': {'address': '5198 Broadway',
'crossStreet': 'at 225th St.',
'lat': 40.87441177110231,
'lng': -73.91027100981574,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87441177110231,
'lng': -73.91027100981574}],
'distance': 240,
'postalCode': '10463',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5198 Broadway (at 225th St.)',
'New York, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1ca941735',
'name': 'Pizza Place',
'pluralName': 'Pizza Places',
'shortName': 'Pizza',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
'suffix': '.png'},
'primary': True}],
'delivery': {'id': '72548',
'url': 'https://www.seamless.com/menu/arturos-pizza-5189-broadway-ave-new-york/72548?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=72548',
'provider': {'name': 'seamless',
'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
'sizes': [40, 50],
'name': '/delivery_provider_seamless_20180129.png'}}},
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4b4429abf964a52037f225e3-1'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4b79cc46f964a520c5122fe3',
'name': 'Tibbett Diner',
'location': {'address': '3033 Tibbett Ave',
'crossStreet': 'btwn 230th & 231st',
'lat': 40.8804044222466,
'lng': -73.90893738006402,
'labeledLatLngs': [{'label': 'display',
'lat': 40.8804044222466,
'lng': -73.90893738006402}],
'distance': 452,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['3033 Tibbett Ave (btwn 230th & 231st)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d147941735',
'name': 'Diner',
'pluralName': 'Diners',
'shortName': 'Diner',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/diner_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4b79cc46f964a520c5122fe3-2'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bb114c4f964a520b9783ce3',
'name': "Sam's Pizza",
'location': {'address': '232 W 231st St',
'crossStreet': 'Godwin Terrace',
'lat': 40.87943501792795,
'lng': -73.90585879861192,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87943501792795,
'lng': -73.90585879861192}],
'distance': 516,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['232 W 231st St (Godwin Terrace)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1ca941735',
'name': 'Pizza Place',
'pluralName': 'Pizza Places',
'shortName': 'Pizza',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4bb114c4f964a520b9783ce3-3'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '55f81cd2498ee903149fcc64',
'name': 'Starbucks',
'location': {'address': '171 W 230th St',
'crossStreet': 'Kimberly Pl',
'lat': 40.87753134921497,
'lng': -73.90558216359267,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87753134921497,
'lng': -73.90558216359267}],
'distance': 441,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['171 W 230th St (Kimberly Pl)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1e0931735',
'name': 'Coffee Shop',
'pluralName': 'Coffee Shops',
'shortName': 'Coffee Shop',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-55f81cd2498ee903149fcc64-4'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '55f751ca498eacc0307d1cfe',
'name': 'Blink Fitness Riverdale',
'location': {'address': '5520 Broadway',
'crossStreet': 'at W 230th St',
'lat': 40.87714687429521,
'lng': -73.90583697267095,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87714687429521,
'lng': -73.90583697267095}],
'distance': 411,
'postalCode': '10463',
'cc': 'US',
'neighborhood': 'Kingsbridge',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5520 Broadway (at W 230th St)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d176941735',
'name': 'Gym',
'pluralName': 'Gyms',
'shortName': 'Gym',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-55f751ca498eacc0307d1cfe-5'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bd8e98811dcc928f865f833',
'name': 'El Malecon',
'location': {'address': '5592 Broadway',
'crossStreet': 'at W 231st St',
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'postalCode': '10468',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Reservoir Ave (197th Street)',
'Bronx, NY 10468',
'United States']},
'categories': [{'id': '4bf58dd8d48988d163941735',
'name': 'Park',
'pluralName': 'Parks',
'shortName': 'Park',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/park_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4dbf5056cda12687c8321adb-80'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4e6e7c16ae60d13edd3ab99b',
'name': 'QCigars',
'location': {'address': '5009 Broadway',
'crossStreet': 'W 214th street',
'lat': 40.869109637103996,
'lng': -73.91700007550557,
'labeledLatLngs': [{'label': 'display',
'lat': 40.869109637103996,
'lng': -73.91700007550557}],
'distance': 985,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5009 Broadway (W 214th street)',
'New York, NY 10034',
'United States']},
'categories': [{'id': '4bf58dd8d48988d123951735',
'name': 'Smoke Shop',
'pluralName': 'Smoke Shops',
'shortName': 'Smoke Shop',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/tobacco_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4e6e7c16ae60d13edd3ab99b-81'}]}]}}
manhattan_venues = pd.DataFrame(columns = ["Venue", "Latitude","Longitude","Address","Category"])
manhattan_venues
| Venue | Latitude | Longitude | Address | Category |
|---|
venues = results['response']['groups'][0]['items']
nearby_venues = json_normalize(venues) # flatten JSON
nearby_venues.head()
| reasons.count | reasons.items | referralId | venue.categories | venue.delivery.id | venue.delivery.provider.icon.name | venue.delivery.provider.icon.prefix | venue.delivery.provider.icon.sizes | venue.delivery.provider.name | venue.delivery.url | venue.id | venue.location.address | venue.location.cc | venue.location.city | venue.location.country | venue.location.crossStreet | venue.location.distance | venue.location.formattedAddress | venue.location.labeledLatLngs | venue.location.lat | venue.location.lng | venue.location.neighborhood | venue.location.postalCode | venue.location.state | venue.name | venue.photos.count | venue.photos.groups | venue.venuePage.id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | [{'summary': 'This spot is popular', 'type': '... | e-0-4baf59e8f964a520a6f93be3-0 | [{'id': '4bf58dd8d48988d102941735', 'name': 'Y... | NaN | NaN | NaN | NaN | NaN | NaN | 4baf59e8f964a520a6f93be3 | 5500 Broadway | US | Bronx | United States | 230th Street | 376 | [5500 Broadway (230th Street), Bronx, NY 10463... | [{'label': 'display', 'lat': 40.87684369079793... | 40.876844 | -73.906204 | NaN | 10463 | NY | Bikram Yoga | 0 | [] | NaN |
| 1 | 0 | [{'summary': 'This spot is popular', 'type': '... | e-0-4b4429abf964a52037f225e3-1 | [{'id': '4bf58dd8d48988d1ca941735', 'name': 'P... | 72548 | /delivery_provider_seamless_20180129.png | https://fastly.4sqi.net/img/general/cap/ | [40, 50] | seamless | https://www.seamless.com/menu/arturos-pizza-51... | 4b4429abf964a52037f225e3 | 5198 Broadway | US | New York | United States | at 225th St. | 240 | [5198 Broadway (at 225th St.), New York, NY 10... | [{'label': 'display', 'lat': 40.87441177110231... | 40.874412 | -73.910271 | NaN | 10463 | NY | Arturo's | 0 | [] | NaN |
| 2 | 0 | [{'summary': 'This spot is popular', 'type': '... | e-0-4b79cc46f964a520c5122fe3-2 | [{'id': '4bf58dd8d48988d147941735', 'name': 'D... | NaN | NaN | NaN | NaN | NaN | NaN | 4b79cc46f964a520c5122fe3 | 3033 Tibbett Ave | US | Bronx | United States | btwn 230th & 231st | 452 | [3033 Tibbett Ave (btwn 230th & 231st), Bronx,... | [{'label': 'display', 'lat': 40.8804044222466,... | 40.880404 | -73.908937 | NaN | 10463 | NY | Tibbett Diner | 0 | [] | NaN |
| 3 | 0 | [{'summary': 'This spot is popular', 'type': '... | e-0-4bb114c4f964a520b9783ce3-3 | [{'id': '4bf58dd8d48988d1ca941735', 'name': 'P... | NaN | NaN | NaN | NaN | NaN | NaN | 4bb114c4f964a520b9783ce3 | 232 W 231st St | US | Bronx | United States | Godwin Terrace | 516 | [232 W 231st St (Godwin Terrace), Bronx, NY 10... | [{'label': 'display', 'lat': 40.87943501792795... | 40.879435 | -73.905859 | NaN | 10463 | NY | Sam's Pizza | 0 | [] | NaN |
| 4 | 0 | [{'summary': 'This spot is popular', 'type': '... | e-0-55f81cd2498ee903149fcc64-4 | [{'id': '4bf58dd8d48988d1e0931735', 'name': 'C... | NaN | NaN | NaN | NaN | NaN | NaN | 55f81cd2498ee903149fcc64 | 171 W 230th St | US | Bronx | United States | Kimberly Pl | 441 | [171 W 230th St (Kimberly Pl), Bronx, NY 10463... | [{'label': 'display', 'lat': 40.87753134921497... | 40.877531 | -73.905582 | NaN | 10463 | NY | Starbucks | 0 | [] | NaN |
venues
[{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4baf59e8f964a520a6f93be3',
'name': 'Bikram Yoga',
'location': {'address': '5500 Broadway',
'crossStreet': '230th Street',
'lat': 40.876843690797934,
'lng': -73.90620384419528,
'labeledLatLngs': [{'label': 'display',
'lat': 40.876843690797934,
'lng': -73.90620384419528}],
'distance': 376,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5500 Broadway (230th Street)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d102941735',
'name': 'Yoga Studio',
'pluralName': 'Yoga Studios',
'shortName': 'Yoga Studio',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/gym_yogastudio_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4baf59e8f964a520a6f93be3-0'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4b4429abf964a52037f225e3',
'name': "Arturo's",
'location': {'address': '5198 Broadway',
'crossStreet': 'at 225th St.',
'lat': 40.87441177110231,
'lng': -73.91027100981574,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87441177110231,
'lng': -73.91027100981574}],
'distance': 240,
'postalCode': '10463',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5198 Broadway (at 225th St.)',
'New York, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1ca941735',
'name': 'Pizza Place',
'pluralName': 'Pizza Places',
'shortName': 'Pizza',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
'suffix': '.png'},
'primary': True}],
'delivery': {'id': '72548',
'url': 'https://www.seamless.com/menu/arturos-pizza-5189-broadway-ave-new-york/72548?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=72548',
'provider': {'name': 'seamless',
'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
'sizes': [40, 50],
'name': '/delivery_provider_seamless_20180129.png'}}},
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4b4429abf964a52037f225e3-1'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4b79cc46f964a520c5122fe3',
'name': 'Tibbett Diner',
'location': {'address': '3033 Tibbett Ave',
'crossStreet': 'btwn 230th & 231st',
'lat': 40.8804044222466,
'lng': -73.90893738006402,
'labeledLatLngs': [{'label': 'display',
'lat': 40.8804044222466,
'lng': -73.90893738006402}],
'distance': 452,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['3033 Tibbett Ave (btwn 230th & 231st)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d147941735',
'name': 'Diner',
'pluralName': 'Diners',
'shortName': 'Diner',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/diner_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4b79cc46f964a520c5122fe3-2'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bb114c4f964a520b9783ce3',
'name': "Sam's Pizza",
'location': {'address': '232 W 231st St',
'crossStreet': 'Godwin Terrace',
'lat': 40.87943501792795,
'lng': -73.90585879861192,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87943501792795,
'lng': -73.90585879861192}],
'distance': 516,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['232 W 231st St (Godwin Terrace)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1ca941735',
'name': 'Pizza Place',
'pluralName': 'Pizza Places',
'shortName': 'Pizza',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4bb114c4f964a520b9783ce3-3'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '55f81cd2498ee903149fcc64',
'name': 'Starbucks',
'location': {'address': '171 W 230th St',
'crossStreet': 'Kimberly Pl',
'lat': 40.87753134921497,
'lng': -73.90558216359267,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87753134921497,
'lng': -73.90558216359267}],
'distance': 441,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['171 W 230th St (Kimberly Pl)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1e0931735',
'name': 'Coffee Shop',
'pluralName': 'Coffee Shops',
'shortName': 'Coffee Shop',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-55f81cd2498ee903149fcc64-4'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '55f751ca498eacc0307d1cfe',
'name': 'Blink Fitness Riverdale',
'location': {'address': '5520 Broadway',
'crossStreet': 'at W 230th St',
'lat': 40.87714687429521,
'lng': -73.90583697267095,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87714687429521,
'lng': -73.90583697267095}],
'distance': 411,
'postalCode': '10463',
'cc': 'US',
'neighborhood': 'Kingsbridge',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5520 Broadway (at W 230th St)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d176941735',
'name': 'Gym',
'pluralName': 'Gyms',
'shortName': 'Gym',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-55f751ca498eacc0307d1cfe-5'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bd8e98811dcc928f865f833',
'name': 'El Malecon',
'location': {'address': '5592 Broadway',
'crossStreet': 'at W 231st St',
'lat': 40.87933806746814,
'lng': -73.90445707056641,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87933806746814,
'lng': -73.90445707056641}],
'distance': 607,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5592 Broadway (at W 231st St)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d144941735',
'name': 'Caribbean Restaurant',
'pluralName': 'Caribbean Restaurants',
'shortName': 'Caribbean',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/caribbean_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4bd8e98811dcc928f865f833-6'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4d9a37d4d97ba1430a07346b',
'name': "Loeser's Delicatessen",
'location': {'address': '214 W 231st St',
'lat': 40.879241887611094,
'lng': -73.9054706858219,
'labeledLatLngs': [{'label': 'display',
'lat': 40.879241887611094,
'lng': -73.9054706858219}],
'distance': 529,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['214 W 231st St',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1c5941735',
'name': 'Sandwich Place',
'pluralName': 'Sandwich Places',
'shortName': 'Sandwiches',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/deli_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4d9a37d4d97ba1430a07346b-7'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '5217dd2811d2d06ccafb77d3',
'name': 'Estrellita Poblana V',
'location': {'address': '240 W 231st St',
'lat': 40.879687039717524,
'lng': -73.906256832975,
'labeledLatLngs': [{'label': 'display',
'lat': 40.879687039717524,
'lng': -73.906256832975}],
'distance': 509,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['240 W 231st St',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1c1941735',
'name': 'Mexican Restaurant',
'pluralName': 'Mexican Restaurants',
'shortName': 'Mexican',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/mexican_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-5217dd2811d2d06ccafb77d3-8'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4b5357adf964a520319827e3',
'name': "Dunkin'",
'location': {'address': '5501 Broadway',
'crossStreet': 'W 230th St',
'lat': 40.87713584201589,
'lng': -73.90666550701411,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87713584201589,
'lng': -73.90666550701411}],
'distance': 342,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5501 Broadway (W 230th St)',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d148941735',
'name': 'Donut Shop',
'pluralName': 'Donut Shops',
'shortName': 'Donuts',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4b5357adf964a520319827e3-9'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '59b17fe23b4e0043787e1a7c',
'name': 'The Bronx Public',
'location': {'address': '170 West 231st Street',
'lat': 40.87837659295958,
'lng': -73.90348117090952,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87837659295958,
'lng': -73.90348117090952}],
'distance': 637,
'postalCode': '10463',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['170 West 231st Street',
'New York, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d11b941735',
'name': 'Pub',
'pluralName': 'Pubs',
'shortName': 'Pub',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/nightlife/pub_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-59b17fe23b4e0043787e1a7c-10'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bce0773c564ef3b6686edf0',
'name': 'Baker Athletic Complex',
'location': {'address': '533 W 218th St',
'crossStreet': 'at Broadway',
'lat': 40.87206097197959,
'lng': -73.91487585478518,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87206097197959,
'lng': -73.91487585478518}],
'distance': 612,
'postalCode': '10034',
'cc': 'US',
'state': 'New York',
'country': 'United States',
'formattedAddress': ['533 W 218th St (at Broadway)',
'NY 10034',
'United States']},
'categories': [{'id': '4f4528bc4b90abdf24c9de85',
'name': 'Athletics & Sports',
'pluralName': 'Athletics & Sports',
'shortName': 'Athletics & Sports',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/sports_outdoors_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4bce0773c564ef3b6686edf0-11'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '49caa774f964a520d1581fe3',
'name': 'Indian Road Café',
'location': {'address': '600 W 218th St',
'crossStreet': 'at Indian Rd',
'lat': 40.87292174447656,
'lng': -73.91845881138549,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87292174447656,
'lng': -73.91845881138549}],
'distance': 770,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['600 W 218th St (at Indian Rd)',
'New York, NY 10034',
'United States']},
'categories': [{'id': '4bf58dd8d48988d16d941735',
'name': 'Café',
'pluralName': 'Cafés',
'shortName': 'Café',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
'suffix': '.png'},
'primary': True}],
'delivery': {'id': '1104394',
'url': 'https://www.seamless.com/menu/indian-road-cafe-600-w-218th-st-new-york/1104394?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=1104394',
'provider': {'name': 'seamless',
'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/',
'sizes': [40, 50],
'name': '/delivery_provider_seamless_20180129.png'}}},
'photos': {'count': 0, 'groups': []},
'venuePage': {'id': '72900166'}},
'referralId': 'e-0-49caa774f964a520d1581fe3-12'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4af8b2c0f964a520460f22e3',
'name': 'Garden Gourmet Market',
'location': {'address': '5665 Broadway',
'crossStreet': 'btwn W 233rd & W 234th St',
'lat': 40.88135023917183,
'lng': -73.90338943716417,
'labeledLatLngs': [{'label': 'display',
'lat': 40.88135023917183,
'lng': -73.90338943716417}],
'distance': 812,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
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'categories': [{'id': '4bf58dd8d48988d1fe931735',
'name': 'Bus Station',
'pluralName': 'Bus Stations',
'shortName': 'Bus Station',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/busstation_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4e9f29c14fc679d8a715eab8-68'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '52dd52bb498e35b966482afb',
'name': 'Muscota Marsh',
'location': {'address': 'West 218th St. and Indian Rd.',
'lat': 40.87315174368968,
'lng': -73.91853739645013,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87315174368968,
'lng': -73.91853739645013}],
'distance': 763,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['West 218th St. and Indian Rd.',
'New York, NY 10034',
'United States']},
'categories': [{'id': '4bf58dd8d48988d163941735',
'name': 'Park',
'pluralName': 'Parks',
'shortName': 'Park',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/park_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-52dd52bb498e35b966482afb-69'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '5622ff65498ee6007d29640b',
'name': 'Food Universe Marketplace',
'location': {'address': '5069 Broadway',
'lat': 40.870538084478596,
'lng': -73.91519744704213,
'labeledLatLngs': [{'label': 'display',
'lat': 40.870538084478596,
'lng': -73.91519744704213}],
'distance': 770,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5069 Broadway',
'New York, NY 10034',
'United States']},
'categories': [{'id': '50be8ee891d4fa8dcc7199a7',
'name': 'Market',
'pluralName': 'Markets',
'shortName': 'Market',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/market_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-5622ff65498ee6007d29640b-70'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '5083fe69e4b03db17714dd37',
'name': 'Bike MS New York City - Inwood Hill Rest Stop',
'location': {'lat': 40.87283852562406,
'lng': -73.91930224528193,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87283852562406,
'lng': -73.91930224528193}],
'distance': 836,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['New York, NY 10034', 'United States']},
'categories': [{'id': '4bf58dd8d48988d163941735',
'name': 'Park',
'pluralName': 'Parks',
'shortName': 'Park',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/park_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-5083fe69e4b03db17714dd37-71'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '57f50cbe498e3b45c7efea57',
'name': 'mta bus stop #100705 (Independance AVE &Henry Hudson Parkway)',
'location': {'lat': 40.880829083641686,
'lng': -73.9189705975229,
'labeledLatLngs': [{'label': 'display',
'lat': 40.880829083641686,
'lng': -73.9189705975229}],
'distance': 846,
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Bronx, NY', 'United States']},
'categories': [{'id': '52f2ab2ebcbc57f1066b8b4f',
'name': 'Bus Stop',
'pluralName': 'Bus Stops',
'shortName': 'Bus Stop',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/busstation_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-57f50cbe498e3b45c7efea57-72'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4c22bc3cfbe5c9b6f4769a21',
'name': 'Arlington Avenue & West 232nd Street',
'location': {'address': 'Arlington Ave',
'crossStreet': 'at W 232nd St',
'lat': 40.88401563216687,
'lng': -73.91252232171007,
'labeledLatLngs': [{'label': 'display',
'lat': 40.88401563216687,
'lng': -73.91252232171007}],
'distance': 845,
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Arlington Ave (at W 232nd St)',
'Bronx, NY',
'United States']},
'categories': [{'id': '52f2ab2ebcbc57f1066b8b4c',
'name': 'Intersection',
'pluralName': 'Intersections',
'shortName': 'Intersection',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/default_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4c22bc3cfbe5c9b6f4769a21-73'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4e2ce957b61cd010838538ef',
'name': 'Spuyten Duyvil Creek',
'location': {'address': 'Bakers Field',
'crossStreet': 'W.218 St',
'lat': 40.87362586210354,
'lng': -73.9201633990417,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87362586210354,
'lng': -73.9201633990417}],
'distance': 863,
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Bakers Field (W.218 St)',
'New York, NY',
'United States']},
'categories': [{'id': '4eb1d4dd4b900d56c88a45fd',
'name': 'River',
'pluralName': 'Rivers',
'shortName': 'River',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/river_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4e2ce957b61cd010838538ef-74'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4c83f3c0e602b1f7d78baa7a',
'name': 'Waterfront DSNY',
'location': {'lat': 40.86847530832377,
'lng': -73.91168747355391,
'labeledLatLngs': [{'label': 'display',
'lat': 40.86847530832377,
'lng': -73.91168747355391}],
'distance': 903,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['New York, NY 10034', 'United States']},
'categories': [{'id': '4bf58dd8d48988d165941735',
'name': 'Scenic Lookout',
'pluralName': 'Scenic Lookouts',
'shortName': 'Scenic Lookout',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/sceniclookout_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4c83f3c0e602b1f7d78baa7a-75'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '513e76fae4b05b7b6b2efd07',
'name': 'Major General John R. Brown Triangle',
'location': {'address': 'Sedgwick',
'crossStreet': 'West Kingsbridge Road',
'lat': 40.86979572756906,
'lng': -73.90444387233786,
'labeledLatLngs': [{'label': 'display',
'lat': 40.86979572756906,
'lng': -73.90444387233786}],
'distance': 916,
'postalCode': '110468',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Sedgwick (West Kingsbridge Road)',
'Bronx, NY 110468',
'United States']},
'categories': [{'id': '4bf58dd8d48988d163941735',
'name': 'Park',
'pluralName': 'Parks',
'shortName': 'Park',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/park_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-513e76fae4b05b7b6b2efd07-76'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4bb7389d46d4a593b130c7c0',
'name': 'Fort Tryon Cafe & Grill',
'location': {'address': '4026 10th Ave',
'crossStreet': 'W 215th St',
'lat': 40.869304489744984,
'lng': -73.91590926235688,
'labeledLatLngs': [{'label': 'display',
'lat': 40.869304489744984,
'lng': -73.91590926235688}],
'distance': 919,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['4026 10th Ave (W 215th St)',
'New York, NY 10034',
'United States']},
'categories': [{'id': '4bf58dd8d48988d14e941735',
'name': 'American Restaurant',
'pluralName': 'American Restaurants',
'shortName': 'American',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/default_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4bb7389d46d4a593b130c7c0-77'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4e4c9e1318388d49816b44d8',
'name': 'La Catrina',
'location': {'address': '155 W Kingsbridge Rd',
'lat': 40.86995978194631,
'lng': -73.9039558462761,
'labeledLatLngs': [{'label': 'display',
'lat': 40.86995978194631,
'lng': -73.9039558462761}],
'distance': 925,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['155 W Kingsbridge Rd',
'Bronx, NY 10463',
'United States']},
'categories': [{'id': '4bf58dd8d48988d1c1941735',
'name': 'Mexican Restaurant',
'pluralName': 'Mexican Restaurants',
'shortName': 'Mexican',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/mexican_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4e4c9e1318388d49816b44d8-78'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4c4b8403f7cc1b8d0676f83f',
'name': 'Jerome Park Reservoir',
'location': {'lat': 40.87258362206072,
'lng': -73.90064260006244,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87258362206072,
'lng': -73.90064260006244}],
'distance': 951,
'postalCode': '10463',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Bronx, NY 10463', 'United States']},
'categories': [{'id': '4bf58dd8d48988d161941735',
'name': 'Lake',
'pluralName': 'Lakes',
'shortName': 'Lake',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/lake_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4c4b8403f7cc1b8d0676f83f-79'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4dbf5056cda12687c8321adb',
'name': "Washington's Walk",
'location': {'crossStreet': '197th Street',
'lat': 40.87255460684972,
'lng': -73.90056259601329,
'labeledLatLngs': [{'label': 'display',
'lat': 40.87255460684972,
'lng': -73.90056259601329}],
'distance': 959,
'postalCode': '10468',
'cc': 'US',
'city': 'Bronx',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['Reservoir Ave (197th Street)',
'Bronx, NY 10468',
'United States']},
'categories': [{'id': '4bf58dd8d48988d163941735',
'name': 'Park',
'pluralName': 'Parks',
'shortName': 'Park',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/parks_outdoors/park_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4dbf5056cda12687c8321adb-80'},
{'reasons': {'count': 0,
'items': [{'summary': 'This spot is popular',
'type': 'general',
'reasonName': 'globalInteractionReason'}]},
'venue': {'id': '4e6e7c16ae60d13edd3ab99b',
'name': 'QCigars',
'location': {'address': '5009 Broadway',
'crossStreet': 'W 214th street',
'lat': 40.869109637103996,
'lng': -73.91700007550557,
'labeledLatLngs': [{'label': 'display',
'lat': 40.869109637103996,
'lng': -73.91700007550557}],
'distance': 985,
'postalCode': '10034',
'cc': 'US',
'city': 'New York',
'state': 'NY',
'country': 'United States',
'formattedAddress': ['5009 Broadway (W 214th street)',
'New York, NY 10034',
'United States']},
'categories': [{'id': '4bf58dd8d48988d123951735',
'name': 'Smoke Shop',
'pluralName': 'Smoke Shops',
'shortName': 'Smoke Shop',
'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/tobacco_',
'suffix': '.png'},
'primary': True}],
'photos': {'count': 0, 'groups': []}},
'referralId': 'e-0-4e6e7c16ae60d13edd3ab99b-81'}]
We have our information in 'Venue.name' ,'venue.categories', 'venue.location.lat', 'venue.location.lng', "venue.location.formattedAddress". We have to a function to Venue.categories to extract the Venue Category.
# function that extracts the category of the venue
def get_category_type(row):
categories_list = row['venue.categories']
if len(categories_list) == 0:
return None
else:
return categories_list[0]['name']
# filter columns
filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']
nearby_venues =nearby_venues.loc[:, filtered_columns]
# filter the category for each row
nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)
nearby_venues.head()
| venue.name | venue.categories | venue.location.lat | venue.location.lng | |
|---|---|---|---|---|
| 0 | Bikram Yoga | Yoga Studio | 40.876844 | -73.906204 |
| 1 | Arturo's | Pizza Place | 40.874412 | -73.910271 |
| 2 | Tibbett Diner | Diner | 40.880404 | -73.908937 |
| 3 | Sam's Pizza | Pizza Place | 40.879435 | -73.905859 |
| 4 | Starbucks | Coffee Shop | 40.877531 | -73.905582 |
print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))
82 venues were returned by Foursquare.
Let's create a function to repeat the same process to all the neighborhoods in each Borough.
def getNearbyVenues(borough,names, latitudes, longitudes, radius=500):
venues_list=[]
for name, lat, lng in zip(names, latitudes, longitudes):
print(name)
# create the API request URL
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
CLIENT_ID,
CLIENT_SECRET,
VERSION,
lat,
lng,
radius,
LIMIT)
# make the GET request
results = requests.get(url).json()
#print(results)
results = results["response"]["groups"][0]["items"]
# return only relevant information for each nearby venue
venues_list.append([(
borough,
name,
lat,
lng,
v['venue']['name'],
v['venue']['location']['lat'],
v['venue']['location']['lng'],
v['venue']['categories'][0]['name']
) for v in results])
nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
nearby_venues.columns = ['Borough','Neighborhood',
'Neighborhood Latitude',
'Neighborhood Longitude',
'Venue',
'Venue Latitude',
'Venue Longitude',
'Venue Category']
return(nearby_venues)
boroughs = newyork_df['Borough_name'].unique().tolist()
boroughs
['Bronx', 'Manhattan', 'Brooklyn', 'Queens', 'Staten Island']
Master_df = pd.DataFrame(columns = ['Borough','Neighborhood',
'Neighborhood Latitude',
'Neighborhood Longitude',
'Venue',
'Venue Latitude',
'Venue Longitude',
'Venue Category'])
borough = boroughs[0]
print('Finding Venues around {} '.format(borough))
borough_data = newyork_df[newyork_df['Borough_name'] == borough].reset_index(drop=True)
#print(borough_data.head())
borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Latitude'],
longitudes=borough_data['Longitude']
)
Master_df = Master_df.append(borough_venues,ignore_index = True)
print('Concatenated {}_venues dataframe to Master dataframe'.format(borough))
print("....................................................................")
Finding Venues around Bronx
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-38-e0742779802c> in <module> 12 borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'], 13 latitudes=borough_data['Latitude'], ---> 14 longitudes=borough_data['Longitude'] 15 ) 16 Master_df = Master_df.append(borough_venues,ignore_index = True) <ipython-input-36-63da8471bdf8> in getNearbyVenues(borough, names, latitudes, longitudes, radius) 18 results = requests.get(url).json() 19 #print(results) ---> 20 results = results["response"]["groups"][0]["items"] 21 22 # return only relevant information for each nearby venue KeyError: 'groups'
borough = boroughs[1]
print('Finding Venues around {} '.format(borough))
borough_data = newyork_df[newyork_df['Borough_name'] == borough].reset_index(drop=True)
#print(borough_data.head())
borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Latitude'],
longitudes=borough_data['Longitude']
)
Master_df = Master_df.append(borough_venues,ignore_index = True)
print('Concatenated {}_venues dataframe to Master dataframe'.format(borough))
print("....................................................................")
Finding Venues around Manhattan
{'meta': {'code': 200, 'requestId': '5d7fc6dab77c77002c0381f6'}, 'response': {'suggestedFilters': {'header': 'Tap to show:', 'filters': [{'name': 'Open now', 'key': 'openNow'}]}, 'headerLocation': 'Marble Hill', 'headerFullLocation': 'Marble Hill, New York', 'headerLocationGranularity': 'neighborhood', 'totalResults': 24, 'suggestedBounds': {'ne': {'lat': 40.88105078329964, 'lng': -73.90471933917806}, 'sw': {'lat': 40.87205077429964, 'lng': -73.91659997808156}}, 'groups': [{'type': 'Recommended Places', 'name': 'recommended', 'items': [{'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4b4429abf964a52037f225e3', 'name': "Arturo's", 'location': {'address': '5198 Broadway', 'crossStreet': 'at 225th St.', 'lat': 40.87441177110231, 'lng': -73.91027100981574, 'labeledLatLngs': [{'label': 'display', 'lat': 40.87441177110231, 'lng': -73.91027100981574}], 'distance': 240, 'postalCode': '10463', 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Ristorante', 'location': {'address': '235 Mulberry St', 'crossStreet': 'btwn Prince & Spring St', 'lat': 40.72270625453151, 'lng': -73.99595719792266, 'labeledLatLngs': [{'label': 'display', 'lat': 40.72270625453151, 'lng': -73.99595719792266}], 'distance': 393, 'postalCode': '10012', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['235 Mulberry St (btwn Prince & Spring St)', 'New York, NY 10012', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d110941735', 'name': 'Italian Restaurant', 'pluralName': 'Italian Restaurants', 'shortName': 'Italian', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/italian_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}, 'venuePage': {'id': '39039277'}}, 'referralId': 'e-0-4cc6222106c25481d7a4a047-92'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '518bb89a5019390b628c31fc', 'name': 'McNally Jackson Store: Goods For The Study', 'location': {'address': '234 Mulberry St', 'crossStreet': 'btwn Prince & Spring', 'lat': 40.722694313325015, 'lng': -73.99598697945778, 'labeledLatLngs': [{'label': 'display', 'lat': 40.722694313325015, 'lng': -73.99598697945778}], 'distance': 391, 'postalCode': '10012', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['234 Mulberry St (btwn Prince & Spring)', 'New York, NY 10012', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d121951735', 'name': 'Paper / Office Supplies Store', 'pluralName': 'Paper / Office Supplies Stores', 'shortName': 'Office Supplies', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/papergoods_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-518bb89a5019390b628c31fc-93'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '5a8b26761f74405d21697e41', 'name': 'Hit House', 'location': {'address': '2 Spring St', 'lat': 40.7210258578459, 'lng': -73.99383718121187, 'labeledLatLngs': [{'label': 'display', 'lat': 40.7210258578459, 'lng': -73.99383718121187}], 'distance': 348, 'postalCode': '10012', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['2 Spring St', 'New York, NY 10012', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d175941735', 'name': 'Gym / Fitness Center', 'pluralName': 'Gyms or Fitness Centers', 'shortName': 'Gym / Fitness', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-5a8b26761f74405d21697e41-94'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '5b4169604a1cc0002c9bdb7c', 'name': 'Grand Seoul', 'location': {'address': '85 Chrystie St', 'crossStreet': 'Hester Street And Grand Atreet', 'lat': 40.71744683908609, 'lng': -73.99425648068716, 'labeledLatLngs': [{'label': 'display', 'lat': 40.71744683908609, 'lng': -73.99425648068716}], 'distance': 331, 'postalCode': '10002', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['85 Chrystie St (Hester Street And Grand Atreet)', 'New York, NY 10002', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d113941735', 'name': 'Korean Restaurant', 'pluralName': 'Korean Restaurants', 'shortName': 'Korean', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/korean_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-5b4169604a1cc0002c9bdb7c-95'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '5ce0352c666116002cc12ea5', 'name': 'Bluestone Lane Cafe', 'location': {'address': '19 Kenmare St', 'lat': 40.720604, 'lng': -73.994399, 'labeledLatLngs': [{'label': 'display', 'lat': 40.720604, 'lng': -73.994399}], 'distance': 283, 'postalCode': '10012', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['19 Kenmare St', 'New York, NY 10012', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d16d941735', 'name': 'Café', 'pluralName': 'Cafés', 'shortName': 'Café', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_', 'suffix': '.png'}, 'primary': True}], 'delivery': {'id': '1245961', 'url': 'https://www.seamless.com/menu/bluestone-lane-19-kenmare-st-new-york/1245961?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=1245961', 'provider': {'name': 'seamless', 'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/', 'sizes': [40, 50], 'name': '/delivery_provider_seamless_20180129.png'}}}, 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-5ce0352c666116002cc12ea5-96'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '3fd66200f964a52070e91ee3', 'name': 'Balthazar', 'location': {'address': '80 Spring St', 'crossStreet': 'at Crosby St', 'lat': 40.72272353497926, 'lng': -73.99816997062293, 'labeledLatLngs': [{'label': 'display', 'lat': 40.72272353497926, 'lng': -73.99816997062293}], 'distance': 385, 'postalCode': '10012', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['80 Spring St (at Crosby St)', 'New York, NY 10012', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d10c941735', 'name': 'French Restaurant', 'pluralName': 'French Restaurants', 'shortName': 'French', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/french_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}, 'venuePage': {'id': '51338430'}}, 'referralId': 'e-0-3fd66200f964a52070e91ee3-97'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '49eb223ff964a520a6661fe3', 'name': 'Tai Pan Bakery 大班', 'location': {'address': '194 Canal St', 'crossStreet': 'btwn Mott & Mulberry St.', 'lat': 40.7168862755231, 'lng': -73.99803662360377, 'labeledLatLngs': [{'label': 'display', 'lat': 40.7168862755231, 'lng': -73.99803662360377}], 'distance': 278, 'postalCode': '10013', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['194 Canal St (btwn Mott & Mulberry St.)', 'New York, NY 10013', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d16a941735', 'name': 'Bakery', 'pluralName': 'Bakeries', 'shortName': 'Bakery', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/bakery_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-49eb223ff964a520a6661fe3-98'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '541468f5498e79de14888e73', 'name': 'UFC NYC - SoHo', 'location': {'address': '277 Canal St', 'crossStreet': 'at Broadway', 'lat': 40.71932383938399, 'lng': -74.00177756502949, 'labeledLatLngs': [{'label': 'display', 'lat': 40.71932383938399, 'lng': -74.00177756502949}], 'distance': 377, 'postalCode': '10013', 'cc': 'US', 'city': 'New York', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['277 Canal St (at Broadway)', 'New York, NY 10013', 'United States']}, 'categories': [{'id': '52f2ab2ebcbc57f1066b8b47', 'name': 'Boxing Gym', 'pluralName': 'Boxing Gyms', 'shortName': 'Boxing Gym', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/building/gym_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-541468f5498e79de14888e73-99'}]}]}}
{'meta': {'code': 429, 'errorType': 'quota_exceeded', 'errorDetail': 'Quota exceeded', 'requestId': '5d7fc6ef6adbf5002c8ce6cc'}, 'response': {}}
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-34-6277409b993e> in <module> 5 borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'], 6 latitudes=borough_data['Latitude'], ----> 7 longitudes=borough_data['Longitude'] 8 ) 9 Master_df = Master_df.append(borough_venues,ignore_index = True) <ipython-input-30-b7e94aeb4336> in getNearbyVenues(borough, names, latitudes, longitudes, radius) 18 results = requests.get(url).json() 19 print(results) ---> 20 results = results["response"]["groups"][0]["items"] 21 22 # return only relevant information for each nearby venue KeyError: 'groups'
borough = boroughs[2]
print('Finding Venues around {} '.format(borough))
borough_data = newyork_df[newyork_df['Borough_name'] == borough].reset_index(drop=True)
#print(borough_data.head())
borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Latitude'],
longitudes=borough_data['Longitude']
)
Master_df = Master_df.append(borough_venues,ignore_index = True)
print('Concatenated {}_venues dataframe to Master dataframe'.format(borough))
print("....................................................................")
borough = boroughs[3]
print('Finding Venues around {} '.format(borough))
borough_data = newyork_df[newyork_df['Borough_name'] == borough].reset_index(drop=True)
#print(borough_data.head())
borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Latitude'],
longitudes=borough_data['Longitude']
)
Master_df = Master_df.append(borough_venues,ignore_index = True)
print('Concatenated {}_venues dataframe to Master dataframe'.format(borough))
print("....................................................................")
borough = boroughs[4]
print('Finding Venues around {} '.format(borough))
borough_data = newyork_df[newyork_df['Borough_name'] == borough].reset_index(drop=True)
#print(borough_data.head())
borough_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Latitude'],
longitudes=borough_data['Longitude']
)
Master_df = Master_df.append(borough_venues,ignore_index = True)
print('Concatenated {}_venues dataframe to Master dataframe'.format(borough))
print("....................................................................")
Master_df consists of all neighbourboods with nearest venues,It's Name,locations and Category.
Master_df.to_csv("Master.csv")
Master_df = pd.read_csv("Master.csv")
Master_df.head()
| Unnamed: 0 | Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Bronx | Wakefield | 40.894705 | -73.847201 | Lollipops Gelato | 40.894123 | -73.845892 | Dessert Shop |
| 1 | 1 | Bronx | Wakefield | 40.894705 | -73.847201 | Rite Aid | 40.896649 | -73.844846 | Pharmacy |
| 2 | 2 | Bronx | Wakefield | 40.894705 | -73.847201 | Carvel Ice Cream | 40.890487 | -73.848568 | Ice Cream Shop |
| 3 | 3 | Bronx | Wakefield | 40.894705 | -73.847201 | Cooler Runnings Jamaican Restaurant Inc | 40.898276 | -73.850381 | Caribbean Restaurant |
| 4 | 4 | Bronx | Wakefield | 40.894705 | -73.847201 | Shell | 40.894187 | -73.845862 | Gas Station |
Master_df.columns
Index(['Unnamed: 0', 'Borough', 'Neighborhood', 'Neighborhood Latitude',
'Neighborhood Longitude', 'Venue', 'Venue Latitude', 'Venue Longitude',
'Venue Category'],
dtype='object')
Master_df.drop(columns = 'Unnamed: 0',inplace = True,axis = 1)
Master_df.head()
| Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| 0 | Bronx | Wakefield | 40.894705 | -73.847201 | Lollipops Gelato | 40.894123 | -73.845892 | Dessert Shop |
| 1 | Bronx | Wakefield | 40.894705 | -73.847201 | Rite Aid | 40.896649 | -73.844846 | Pharmacy |
| 2 | Bronx | Wakefield | 40.894705 | -73.847201 | Carvel Ice Cream | 40.890487 | -73.848568 | Ice Cream Shop |
| 3 | Bronx | Wakefield | 40.894705 | -73.847201 | Cooler Runnings Jamaican Restaurant Inc | 40.898276 | -73.850381 | Caribbean Restaurant |
| 4 | Bronx | Wakefield | 40.894705 | -73.847201 | Shell | 40.894187 | -73.845862 | Gas Station |
car_df = Master_df[Master_df['Venue Category'].str.contains('[Cc]ar')]
car_df.head()
| Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| 3 | Bronx | Wakefield | 40.894705 | -73.847201 | Cooler Runnings Jamaican Restaurant Inc | 40.898276 | -73.850381 | Caribbean Restaurant |
| 26 | Bronx | Eastchester | 40.887556 | -73.827806 | Fish & Ting | 40.885539 | -73.829151 | Caribbean Restaurant |
| 32 | Bronx | Eastchester | 40.887556 | -73.827806 | Golden Krust Caribbean Restaurant | 40.888543 | -73.831278 | Caribbean Restaurant |
| 36 | Bronx | Eastchester | 40.887556 | -73.827806 | Royal Caribbean Bakery | 40.888252 | -73.831457 | Caribbean Restaurant |
| 64 | Bronx | Kingsbridge | 40.881687 | -73.902818 | El Malecon | 40.879338 | -73.904457 | Caribbean Restaurant |
car_df['Venue Category'].unique()
array(['Caribbean Restaurant', 'Rental Car Location', 'Daycare',
'Child Care Service'], dtype=object)
rental_car = car_df[car_df['Venue Category'] == 'Rental Car Location']
rental_car.head()
| Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| 91 | Bronx | Kingsbridge | 40.881687 | -73.902818 | Enterprise Rent-A-Car | 40.879866 | -73.903847 | Rental Car Location |
| 137 | Bronx | Woodlawn | 40.898273 | -73.867315 | Enterprise Rent-A-Car | 40.896091 | -73.865364 | Rental Car Location |
| 515 | Bronx | Melrose | 40.819754 | -73.909422 | Zipcar 155 St & 3 Ave - Bronx (MPG) | 40.818924 | -73.914068 | Rental Car Location |
| 826 | Bronx | Belmont | 40.857277 | -73.888452 | Enterprise Rent-A-Car | 40.858491 | -73.884325 | Rental Car Location |
| 1011 | Bronx | Pelham Gardens | 40.862966 | -73.841612 | Budget Truck Rental | 40.865890 | -73.837101 | Rental Car Location |
Let's Visualize them on a heat map.
from folium import plugins
m = folium.Map([40.7127281, -74.0060152], zoom_start=11)
# mark each station as a point
for index, row in rental_car.iterrows():
folium.CircleMarker([row['Venue Latitude'], row['Venue Longitude']],
radius=2,
popup=row['Venue'],
fill_color="#3db7e4", # divvy color
).add_to(m)
# convert to (n, 2) nd-array format for heatmap
CarArr = rental_car[['Venue Latitude', 'Venue Longitude']].as_matrix()
# plot heatmap
m.add_children(plugins.HeatMap(CarArr, radius=15))
m
C:\Users\Welcome\Anaconda3\lib\site-packages\ipykernel_launcher.py:11: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead. # This is added back by InteractiveShellApp.init_path() C:\Users\Welcome\Anaconda3\lib\site-packages\ipykernel_launcher.py:14: FutureWarning: Method `add_children` is deprecated. Please use `add_child` instead.
Let's figure out which Neighbourhoods has these businesses and then suggest a neighbourhood for opening a Car Rental Business.
The number of rental car businesses are 22 in entire Newyork city and Neighbourhood Stein way has the highest number of rental car shops.
Let's superimpose neighbourhoods on top of heat map.
for index, row in rental_car.iterrows():
folium.Marker([row['Neighborhood Latitude'], row['Neighborhood Longitude']],
radius=2,
popup=row['Neighborhood'],
).add_to(m)
m
Let's explore venues around these neighborhoods and see if we can get some insights.
borough_data = rental_car
#print(borough_data.head())
car_rental_venues = getNearbyVenues(borough,names=borough_data['Neighborhood'],
latitudes=borough_data['Neighborhood Latitude'],
longitudes=borough_data['Neighborhood Longitude']
)
print("Found all Venues in those neighborhoods where there are car rental businesses.")
print("....................................................................")
{'meta': {'code': 200, 'requestId': '5d7ff54bf1110b002be97d8d'}, 'response': {'suggestedFilters': {'header': 'Tap to show:', 'filters': [{'name': 'Open now', 'key': 'openNow'}, {'name': '$-$$$$', 'key': 'price'}]}, 'headerLocation': 'Kingsbridge', 'headerFullLocation': 'Kingsbridge, Bronx', 'headerLocationGranularity': 'neighborhood', 'totalResults': 69, 'suggestedBounds': {'ne': {'lat': 40.88618737570521, 'lng': -73.89687720680473}, 'sw': {'lat': 40.87718736670521, 'lng': -73.90875876768737}}, 'groups': [{'type': 'Recommended Places', 'name': 'recommended', 'items': [{'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4af8b2c0f964a520460f22e3', 'name': 'Garden Gourmet Market', 'location': {'address': '5665 Broadway', 'crossStreet': 'btwn W 233rd & W 234th St', 'lat': 40.88135023917183, 'lng': -73.90338943716417, 'labeledLatLngs': [{'label': 'display', 'lat': 40.88135023917183, 'lng': -73.90338943716417}], 'distance': 61, 'postalCode': '10463', 'cc': 'US', 'city': 'Bronx', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['5665 Broadway (btwn W 233rd & W 234th St)', 'Bronx, NY 10463', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d1f5941735', 'name': 'Gourmet Shop', 'pluralName': 'Gourmet Shops', 'shortName': 'Gourmet', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/food_gourmet_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4af8b2c0f964a520460f22e3-0'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '55297008498e9286460da191', 'name': 'Leche y Miel', 'location': {'address': '5761 Broadway', 'crossStreet': '236th Street', 'lat': 40.88370946311038, 'lng': -73.90160581178267, 'labeledLatLngs': [{'label': 'display', 'lat': 40.88370946311038, 'lng': -73.90160581178267}], 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'venue': {'id': '4c1010373ce120a1127a071c', 'name': 'MTA Bus - Bay St & Victory Bl (S46/S48/S51/S61/S62/S66/S74/S76/S78/S81/S84/S86/S91/S92/S96)', 'location': {'address': 'Bay St', 'crossStreet': 'at Victory Blvd', 'lat': 40.63735189825941, 'lng': -74.0762269567312, 'labeledLatLngs': [{'label': 'display', 'lat': 40.63735189825941, 'lng': -74.0762269567312}], 'distance': 365, 'postalCode': '10301', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['Bay St (at Victory Blvd)', 'Staten Island, NY 10301', 'United States']}, 'categories': [{'id': '52f2ab2ebcbc57f1066b8b4f', 'name': 'Bus Stop', 'pluralName': 'Bus Stops', 'shortName': 'Bus Stop', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/busstation_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4c1010373ce120a1127a071c-22'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bda145363c5c9b6669b2268', 'name': 'Top tomato super store', 'location': {'address': 'Bay street', 'lat': 40.637481958192524, 'lng': -74.07611796628325, 'labeledLatLngs': [{'label': 'display', 'lat': 40.637481958192524, 'lng': -74.07611796628325}], 'distance': 375, 'postalCode': '10301', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['Bay street', 'Staten Island, NY 10301', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d118951735', 'name': 'Grocery Store', 'pluralName': 'Grocery Stores', 'shortName': 'Grocery Store', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/food_grocery_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bda145363c5c9b6669b2268-23'}]}]}}
{'meta': {'code': 200, 'requestId': '5d7ff55af9dbde0038d30755'}, 'response': {'suggestedFilters': {'header': 'Tap to show:', 'filters': [{'name': 'Open now', 'key': 'openNow'}]}, 'headerLocation': 'Current map view', 'headerFullLocation': 'Current map view', 'headerLocationGranularity': 'unknown', 'totalResults': 13, 'suggestedBounds': {'ne': {'lat': 40.52919938061813, 'lng': -74.21392204199819}, 'sw': {'lat': 40.520199371618126, 'lng': -74.22574009033735}}, 'groups': [{'type': 'Recommended Places', 'name': 'recommended', 'items': [{'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bc65ec3b387d13adcd4c1e8', 'name': "Ralph's Ices", 'location': {'address': '6272 Amboy Rd', 'lat': 40.52396579948754, 'lng': -74.21501488824632, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52396579948754, 'lng': -74.21501488824632}], 'distance': 415, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6272 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d1c9941735', 'name': 'Ice Cream Shop', 'pluralName': 'Ice Cream Shops', 'shortName': 'Ice Cream', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/icecream_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bc65ec3b387d13adcd4c1e8-0'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4c0bdf0effb8c9b66edf6d61', 'name': 'Il Forno', 'location': {'address': '6382 Amboy Rd', 'lat': 40.52222147622361, 'lng': -74.2166513448245, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52222147622361, 'lng': -74.2166513448245}], 'distance': 385, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6382 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d1ca941735', 'name': 'Pizza Place', 'pluralName': 'Pizza Places', 'shortName': 'Pizza', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_', 'suffix': '.png'}, 'primary': True}], 'delivery': {'id': '311114', 'url': 'https://www.seamless.com/menu/il-forno-pizzeria-6382-amboy-rd-staten-island/311114?affiliate=1131&utm_source=foursquare-affiliate-network&utm_medium=affiliate&utm_campaign=1131&utm_content=311114', 'provider': {'name': 'seamless', 'icon': {'prefix': 'https://fastly.4sqi.net/img/general/cap/', 'sizes': [40, 50], 'name': '/delivery_provider_seamless_20180129.png'}}}, 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4c0bdf0effb8c9b66edf6d61-1'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4e3ead59b0fb875af8721eb2', 'name': 'Dollar Tree', 'location': {'address': '6410 Amboy Rd.', 'lat': 40.521540984414784, 'lng': -74.215762993857, 'labeledLatLngs': [{'label': 'display', 'lat': 40.521540984414784, 'lng': -74.215762993857}], 'distance': 492, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6410 Amboy Rd.', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '52dea92d3cf9994f4e043dbb', 'name': 'Discount Store', 'pluralName': 'Discount Stores', 'shortName': 'Discount Store', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/discountstore_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4e3ead59b0fb875af8721eb2-2'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bb759121344b713e0999e04', 'name': 'Enterprise Rent-A-Car', 'location': {'address': '6381 Amboy Rd', 'lat': 40.52223057773637, 'lng': -74.21699099619212, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52223057773637, 'lng': -74.21699099619212}], 'distance': 365, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6381 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d1ef941735', 'name': 'Rental Car Location', 'pluralName': 'Rental Car Locations', 'shortName': 'Rental Car', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/airport_rentalcar_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bb759121344b713e0999e04-3'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bb120b3f964a520a97c3ce3', 'name': "Wendy's", 'location': {'address': '6420 Amboy Rd', 'lat': 40.5211984, 'lng': -74.2167862, 'labeledLatLngs': [{'label': 'display', 'lat': 40.5211984, 'lng': -74.2167862}], 'distance': 467, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6420 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d16e941735', 'name': 'Fast Food Restaurant', 'pluralName': 'Fast Food Restaurants', 'shortName': 'Fast Food', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/fastfood_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bb120b3f964a520a97c3ce3-4'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4ccc5a25ba0a5481f4bc3c59', 'name': 'Chase Bank', 'location': {'address': '6390 Amboy Rd', 'lat': 40.522261, 'lng': -74.215506, 'labeledLatLngs': [{'label': 'display', 'lat': 40.522261, 'lng': -74.215506}], 'distance': 455, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6390 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d10a951735', 'name': 'Bank', 'pluralName': 'Banks', 'shortName': 'Bank', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/financial_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4ccc5a25ba0a5481f4bc3c59-5'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4bd9963f0115c9b64acb7780', 'name': "Dunkin'", 'location': {'address': '6390 Amboy Rd', 'lat': 40.522202850022914, 'lng': -74.21590964924121, 'labeledLatLngs': [{'label': 'display', 'lat': 40.522202850022914, 'lng': -74.21590964924121}], 'distance': 432, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6390 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d148941735', 'name': 'Donut Shop', 'pluralName': 'Donut Shops', 'shortName': 'Donuts', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4bd9963f0115c9b64acb7780-6'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4e185eb9d1648b834831475e', 'name': "Dunkin'", 'location': {'address': '2965 Veterans Rd W', 'lat': 40.52816866195241, 'lng': -74.2166461412425, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52816866195241, 'lng': -74.2166461412425}], 'distance': 470, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['2965 Veterans Rd W', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d148941735', 'name': 'Donut Shop', 'pluralName': 'Donut Shops', 'shortName': 'Donuts', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/donuts_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4e185eb9d1648b834831475e-7'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4f3e6c5600394a3349ee67d8', 'name': 'Amboy Wines and Liquors', 'location': {'address': '6390 Amboy Rd', 'crossStreet': 'Waldbaums Plaza', 'lat': 40.52200728209026, 'lng': -74.21561396058975, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52200728209026, 'lng': -74.21561396058975}], 'distance': 465, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6390 Amboy Rd (Waldbaums Plaza)', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d186941735', 'name': 'Liquor Store', 'pluralName': 'Liquor Stores', 'shortName': 'Liquor Store', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/food_liquor_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-4f3e6c5600394a3349ee67d8-8'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '55d69528498e8636026212f3', 'name': 'WELCOME TO NEW YORK', 'location': {'lat': 40.52582761645698, 'lng': -74.2252091639015, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52582761645698, 'lng': -74.2252091639015}], 'distance': 472, 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['Staten Island, NY', 'United States']}, 'categories': [{'id': '52f2ab2ebcbc57f1066b8b4e', 'name': 'Toll Plaza', 'pluralName': 'Toll Plazas', 'shortName': 'Toll Plaza', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/default_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-55d69528498e8636026212f3-9'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '4dc3f7a8d4c0ad9c0f61d5a3', 'name': 'Flowers by Bernard', 'location': {'address': '6390 Amboy Rd', 'lat': 40.52189700048952, 'lng': -74.21562962814777, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52189700048952, 'lng': -74.21562962814777}], 'distance': 472, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6390 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d11b951735', 'name': 'Flower Shop', 'pluralName': 'Flower Shops', 'shortName': 'Flower Shop', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/shops/flowershop_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}, 'venuePage': {'id': '39934773'}}, 'referralId': 'e-0-4dc3f7a8d4c0ad9c0f61d5a3-10'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '50acc21ae4b013e1bd00424b', 'name': 'MTA Bus - Outerbridge & Park and Ride (X17/X22/X22A)', 'location': {'address': 'W Shore Expy', 'crossStreet': 'at S Service Rd', 'lat': 40.52267477859878, 'lng': -74.22476781846, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52267477859878, 'lng': -74.22476781846}], 'distance': 474, 'postalCode': '10309', 'cc': 'US', 'neighborhood': 'Pleasant Plains', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['W Shore Expy (at S Service Rd)', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '52f2ab2ebcbc57f1066b8b4f', 'name': 'Bus Stop', 'pluralName': 'Bus Stops', 'shortName': 'Bus Stop', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/travel/busstation_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-50acc21ae4b013e1bd00424b-11'}, {'reasons': {'count': 0, 'items': [{'summary': 'This spot is popular', 'type': 'general', 'reasonName': 'globalInteractionReason'}]}, 'venue': {'id': '509e972be4b0eaa915fa6107', 'name': "Ambrosino's", 'location': {'address': '6410 Amboy Rd', 'lat': 40.52166243322666, 'lng': -74.21548212336818, 'labeledLatLngs': [{'label': 'display', 'lat': 40.52166243322666, 'lng': -74.21548212336818}], 'distance': 499, 'postalCode': '10309', 'cc': 'US', 'city': 'Staten Island', 'state': 'NY', 'country': 'United States', 'formattedAddress': ['6410 Amboy Rd', 'Staten Island, NY 10309', 'United States']}, 'categories': [{'id': '4bf58dd8d48988d1ca941735', 'name': 'Pizza Place', 'pluralName': 'Pizza Places', 'shortName': 'Pizza', 'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/pizza_', 'suffix': '.png'}, 'primary': True}], 'photos': {'count': 0, 'groups': []}}, 'referralId': 'e-0-509e972be4b0eaa915fa6107-12'}]}]}}
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-70-69a69b0d5ed7> in <module> 7 8 ----> 9 Print("Found all Venues in those neighborhoods where there are car rental businesses.") 10 print("....................................................................") NameError: name 'Print' is not defined
car_rental_venues.head()
| Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | |
|---|---|---|---|---|---|---|---|---|
| 0 | Bronx | Kingsbridge | 40.881687 | -73.902818 | Garden Gourmet Market | 40.881350 | -73.903389 | Gourmet Shop |
| 1 | Bronx | Kingsbridge | 40.881687 | -73.902818 | Leche y Miel | 40.883709 | -73.901606 | Latin American Restaurant |
| 2 | Bronx | Kingsbridge | 40.881687 | -73.902818 | Kingsbridge Social Club | 40.884545 | -73.901964 | Pizza Place |
| 3 | Bronx | Kingsbridge | 40.881687 | -73.902818 | El Malecon | 40.879338 | -73.904457 | Caribbean Restaurant |
| 4 | Bronx | Kingsbridge | 40.881687 | -73.902818 | Sam's Pizza | 40.879435 | -73.905859 | Pizza Place |
car_rental_venues_onehot = pd.get_dummies(car_rental_venues[['Venue Category']], prefix="", prefix_sep="")
# add neighborhood column back to dataframe
car_rental_venues_onehot['Neighborhood'] = car_rental_venues['Neighborhood']
# move neighborhood column to the first column
fixed_columns = [car_rental_venues_onehot.columns[-1]] + list(car_rental_venues_onehot.columns[:-1])
car_rental_venues_onehot = car_rental_venues_onehot[fixed_columns]
car_rental_venues_onehot.head()
| Neighborhood | Accessories Store | American Restaurant | Antique Shop | Arepa Restaurant | Argentinian Restaurant | Art Gallery | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auto Workshop | BBQ Joint | Bagel Shop | Bakery | Bank | Bar | Beer Bar | Beer Store | Bike Rental / Bike Share | Boat or Ferry | Bookstore | Boutique | Boxing Gym | Breakfast Spot | Brewery | Bubble Tea Shop | Buffet | Burger Joint | Burrito Place | Bus Station | Bus Stop | Café | Cajun / Creole Restaurant | Candy Store | Cantonese Restaurant | Caribbean Restaurant | Cheese Shop | Chinese Restaurant | Clothing Store | Cocktail Bar | Coffee Shop | Concert Hall | Convenience Store | Cosmetics Shop | Coworking Space | Dance Studio | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Donut Shop | Dry Cleaner | Eastern European Restaurant | Electronics Store | Falafel Restaurant | Fast Food Restaurant | Field | Fish & Chips Shop | Fish Market | Flower Shop | Food & Drink Shop | Food Court | Food Truck | French Restaurant | Fried Chicken Joint | Frozen Yogurt Shop | Fruit & Vegetable Store | Furniture / Home Store | Garden Center | Gas Station | Gastropub | General Entertainment | German Restaurant | Gift Shop | Gourmet Shop | Grocery Store | Gym | Gym / Fitness Center | Gymnastics Gym | Halal Restaurant | Hardware Store | Health & Beauty Service | Historic Site | Home Service | Hookah Bar | Hotel | Hotel Bar | Ice Cream Shop | Indian Restaurant | Irish Pub | Italian Restaurant | Japanese Restaurant | Jewelry Store | Juice Bar | Karaoke Bar | Kebab Restaurant | Kids Store | Lake | Latin American Restaurant | Laundry Service | Liquor Store | Lounge | Market | Martial Arts Dojo | Mattress Store | Mediterranean Restaurant | Men's Store | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Motorcycle Shop | Moving Target | Music Store | Music Venue | Nail Salon | New American Restaurant | Nightclub | Optical Shop | Organic Grocery | Other Repair Shop | Outdoor Sculpture | Paper / Office Supplies Store | Park | Performing Arts Venue | Peruvian Restaurant | Pet Store | Pharmacy | Piano Bar | Pie Shop | Pizza Place | Playground | Plaza | Polish Restaurant | Pool | Print Shop | Pub | Ramen Restaurant | Record Shop | Recording Studio | Rental Car Location | Rest Area | Restaurant | Rock Club | Russian Restaurant | Salon / Barbershop | Sandwich Place | Scenic Lookout | School | Sculpture Garden | Seafood Restaurant | Shipping Store | Shoe Store | Shop & Service | Smoke Shop | Spa | Spanish Restaurant | Sporting Goods Shop | Sports Bar | Sri Lankan Restaurant | Steakhouse | Supermarket | Supplement Shop | Surf Spot | Sushi Restaurant | Tailor Shop | Tapas Restaurant | Tattoo Parlor | Tea Room | Tennis Court | Thai Restaurant | Theater | Thrift / Vintage Store | Tiki Bar | Toll Plaza | Trail | Train Station | Turkish Restaurant | Vape Store | Vegetarian / Vegan Restaurant | Video Game Store | Video Store | Vietnamese Restaurant | Warehouse Store | Whisky Bar | Wine Bar | Wine Shop | Wings Joint | Women's Store | Yoga Studio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Kingsbridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | Kingsbridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | Kingsbridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | Kingsbridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | Kingsbridge | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category.
car_grouped = car_rental_venues_onehot.groupby('Neighborhood').mean().reset_index()
car_grouped.head()
| Neighborhood | Accessories Store | American Restaurant | Antique Shop | Arepa Restaurant | Argentinian Restaurant | Art Gallery | Arts & Crafts Store | Asian Restaurant | Athletics & Sports | Auto Workshop | BBQ Joint | Bagel Shop | Bakery | Bank | Bar | Beer Bar | Beer Store | Bike Rental / Bike Share | Boat or Ferry | Bookstore | Boutique | Boxing Gym | Breakfast Spot | Brewery | Bubble Tea Shop | Buffet | Burger Joint | Burrito Place | Bus Station | Bus Stop | Café | Cajun / Creole Restaurant | Candy Store | Cantonese Restaurant | Caribbean Restaurant | Cheese Shop | Chinese Restaurant | Clothing Store | Cocktail Bar | Coffee Shop | Concert Hall | Convenience Store | Cosmetics Shop | Coworking Space | Dance Studio | Deli / Bodega | Department Store | Dessert Shop | Diner | Discount Store | Donut Shop | Dry Cleaner | Eastern European Restaurant | Electronics Store | Falafel Restaurant | Fast Food Restaurant | Field | Fish & Chips Shop | Fish Market | Flower Shop | Food & Drink Shop | Food Court | Food Truck | French Restaurant | Fried Chicken Joint | Frozen Yogurt Shop | Fruit & Vegetable Store | Furniture / Home Store | Garden Center | Gas Station | Gastropub | General Entertainment | German Restaurant | Gift Shop | Gourmet Shop | Grocery Store | Gym | Gym / Fitness Center | Gymnastics Gym | Halal Restaurant | Hardware Store | Health & Beauty Service | Historic Site | Home Service | Hookah Bar | Hotel | Hotel Bar | Ice Cream Shop | Indian Restaurant | Irish Pub | Italian Restaurant | Japanese Restaurant | Jewelry Store | Juice Bar | Karaoke Bar | Kebab Restaurant | Kids Store | Lake | Latin American Restaurant | Laundry Service | Liquor Store | Lounge | Market | Martial Arts Dojo | Mattress Store | Mediterranean Restaurant | Men's Store | Mexican Restaurant | Middle Eastern Restaurant | Miscellaneous Shop | Mobile Phone Shop | Motorcycle Shop | Moving Target | Music Store | Music Venue | Nail Salon | New American Restaurant | Nightclub | Optical Shop | Organic Grocery | Other Repair Shop | Outdoor Sculpture | Paper / Office Supplies Store | Park | Performing Arts Venue | Peruvian Restaurant | Pet Store | Pharmacy | Piano Bar | Pie Shop | Pizza Place | Playground | Plaza | Polish Restaurant | Pool | Print Shop | Pub | Ramen Restaurant | Record Shop | Recording Studio | Rental Car Location | Rest Area | Restaurant | Rock Club | Russian Restaurant | Salon / Barbershop | Sandwich Place | Scenic Lookout | School | Sculpture Garden | Seafood Restaurant | Shipping Store | Shoe Store | Shop & Service | Smoke Shop | Spa | Spanish Restaurant | Sporting Goods Shop | Sports Bar | Sri Lankan Restaurant | Steakhouse | Supermarket | Supplement Shop | Surf Spot | Sushi Restaurant | Tailor Shop | Tapas Restaurant | Tattoo Parlor | Tea Room | Tennis Court | Thai Restaurant | Theater | Thrift / Vintage Store | Tiki Bar | Toll Plaza | Trail | Train Station | Turkish Restaurant | Vape Store | Vegetarian / Vegan Restaurant | Video Game Store | Video Store | Vietnamese Restaurant | Warehouse Store | Whisky Bar | Wine Bar | Wine Shop | Wings Joint | Women's Store | Yoga Studio | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Bath Beach | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.021277 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.021277 | 0.021277 | 0.000000 | 0.0 | 0.0 | 0.021277 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.042553 | 0.000000 | 0.021277 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.021277 | 0.000000 | 0.00 | 0.063830 | 0.021277 | 0.000000 | 0.021277 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.021277 | 0.000000 | 0.021277 | 0.021277 | 0.00 | 0.042553 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.042553 | 0.0 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.021277 | 0.0 | 0.0 | 0.021277 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.021277 | 0.000000 | 0.000000 | 0.021277 | 0.000000 | 0.0 | 0.042553 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.021277 | 0.042553 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.021277 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.021277 | 0.000000 | 0.021277 | 0.000000 | 0.085106 | 0.00 | 0.000000 | 0.042553 | 0.021277 | 0.00 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.021277 | 0.0 | 0.021277 | 0.0 | 0.021277 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.021277 | 0.042553 | 0.000000 | 0.0 | 0.00 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.021277 | 0.0 | 0.000000 | 0.021277 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.0 | 0.000000 |
| 1 | Belmont | 0.0 | 0.010000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.050000 | 0.030000 | 0.020000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.020000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.000000 | 0.020000 | 0.000000 | 0.010000 | 0.000000 | 0.0 | 0.000000 | 0.080000 | 0.010000 | 0.030000 | 0.010000 | 0.01 | 0.030000 | 0.000000 | 0.01 | 0.01 | 0.000000 | 0.010000 | 0.0 | 0.0 | 0.02 | 0.000000 | 0.02 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.020000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.010000 | 0.020000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.000000 | 0.0 | 0.180000 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.030000 | 0.000000 | 0.010000 | 0.010000 | 0.0 | 0.02 | 0.000000 | 0.020000 | 0.000000 | 0.01 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.010000 | 0.000000 | 0.000000 | 0.000000 | 0.010000 | 0.01 | 0.000000 | 0.090000 | 0.000000 | 0.01 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.010000 | 0.0 | 0.010000 | 0.0 | 0.000000 | 0.0 | 0.020000 | 0.0 | 0.0 | 0.000000 | 0.010000 | 0.0 | 0.01 | 0.000000 | 0.01 | 0.000000 | 0.02 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.01 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.01 | 0.0 | 0.000000 |
| 2 | Boerum Hill | 0.0 | 0.011364 | 0.011364 | 0.0 | 0.0 | 0.000000 | 0.022727 | 0.000000 | 0.011364 | 0.000000 | 0.0 | 0.000000 | 0.022727 | 0.011364 | 0.034091 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.011364 | 0.011364 | 0.000000 | 0.0 | 0.0 | 0.011364 | 0.000000 | 0.011364 | 0.011364 | 0.0 | 0.0 | 0.000000 | 0.011364 | 0.0 | 0.000000 | 0.011364 | 0.00 | 0.011364 | 0.000000 | 0.022727 | 0.045455 | 0.011364 | 0.000000 | 0.022727 | 0.0 | 0.045455 | 0.022727 | 0.011364 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.011364 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.00 | 0.011364 | 0.00 | 0.011364 | 0.000000 | 0.034091 | 0.0 | 0.0 | 0.0 | 0.034091 | 0.011364 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.011364 | 0.000000 | 0.022727 | 0.0 | 0.022727 | 0.0 | 0.011364 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.011364 | 0.000000 | 0.000000 | 0.011364 | 0.0 | 0.011364 | 0.011364 | 0.011364 | 0.011364 | 0.000000 | 0.000000 | 0.022727 | 0.0 | 0.000000 | 0.0 | 0.011364 | 0.000000 | 0.011364 | 0.022727 | 0.0 | 0.00 | 0.011364 | 0.011364 | 0.022727 | 0.00 | 0.000000 | 0.000000 | 0.0 | 0.011364 | 0.011364 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.011364 | 0.011364 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.011364 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.034091 | 0.0 | 0.0 | 0.000000 | 0.011364 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.034091 | 0.00 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.011364 | 0.000000 | 0.0 | 0.00 | 0.0 | 0.011364 | 0.011364 | 0.011364 | 0.011364 | 0.011364 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.011364 | 0.00 | 0.0 | 0.022727 |
| 3 | Clinton Hill | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.010753 | 0.021505 | 0.000000 | 0.000000 | 0.010753 | 0.0 | 0.021505 | 0.010753 | 0.010753 | 0.010753 | 0.0 | 0.0 | 0.021505 | 0.0 | 0.000000 | 0.010753 | 0.010753 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.010753 | 0.000000 | 0.0 | 0.0 | 0.010753 | 0.010753 | 0.0 | 0.000000 | 0.021505 | 0.00 | 0.021505 | 0.010753 | 0.021505 | 0.000000 | 0.000000 | 0.010753 | 0.000000 | 0.0 | 0.000000 | 0.021505 | 0.000000 | 0.010753 | 0.021505 | 0.00 | 0.010753 | 0.000000 | 0.00 | 0.00 | 0.010753 | 0.000000 | 0.0 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.010753 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.010753 | 0.010753 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.010753 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.010753 | 0.032258 | 0.0 | 0.053763 | 0.032258 | 0.000000 | 0.010753 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.010753 | 0.0 | 0.000000 | 0.021505 | 0.010753 | 0.000000 | 0.0 | 0.00 | 0.000000 | 0.043011 | 0.010753 | 0.00 | 0.000000 | 0.010753 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.010753 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.021505 | 0.000000 | 0.00 | 0.010753 | 0.053763 | 0.010753 | 0.00 | 0.0 | 0.0 | 0.0 | 0.010753 | 0.0 | 0.0 | 0.0 | 0.010753 | 0.0 | 0.032258 | 0.0 | 0.000000 | 0.0 | 0.021505 | 0.0 | 0.0 | 0.010753 | 0.000000 | 0.0 | 0.00 | 0.010753 | 0.00 | 0.010753 | 0.00 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.010753 | 0.0 | 0.000000 | 0.021505 | 0.010753 | 0.0 | 0.00 | 0.0 | 0.000000 | 0.032258 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.010753 | 0.000000 | 0.000000 | 0.010753 | 0.0 | 0.0 | 0.0 | 0.032258 | 0.00 | 0.0 | 0.032258 |
| 4 | College Point | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.069767 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.046512 | 0.023256 | 0.046512 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.023256 | 0.000000 | 0.023256 | 0.0 | 0.0 | 0.023256 | 0.000000 | 0.0 | 0.000000 | 0.023256 | 0.00 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.0 | 0.000000 | 0.093023 | 0.000000 | 0.023256 | 0.023256 | 0.00 | 0.023256 | 0.000000 | 0.00 | 0.00 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.023256 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.023256 | 0.023256 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.046512 | 0.000000 | 0.000000 | 0.0 | 0.046512 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.00 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.023256 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.023256 | 0.000000 | 0.000000 | 0.000000 | 0.046512 | 0.00 | 0.000000 | 0.093023 | 0.000000 | 0.00 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.023256 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.023256 | 0.0 | 0.0 | 0.000000 | 0.023256 | 0.0 | 0.00 | 0.000000 | 0.00 | 0.000000 | 0.00 | 0.023256 | 0.0 | 0.0 | 0.023256 | 0.023256 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.00 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.023256 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.0 | 0.000000 |
num_top_venues = 5
for hood in car_grouped['Neighborhood']:
print("----"+hood+"----")
temp = car_grouped[car_grouped['Neighborhood'] == hood].T.reset_index()
temp.columns = ['venue','freq']
temp = temp.iloc[1:]
temp['freq'] = temp['freq'].astype(float)
temp = temp.round({'freq': 2})
print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))
print('\n')
----Bath Beach----
venue freq
0 Pharmacy 0.09
1 Chinese Restaurant 0.06
2 Pizza Place 0.04
3 Kids Store 0.04
4 Italian Restaurant 0.04
----Belmont----
venue freq
0 Italian Restaurant 0.18
1 Pizza Place 0.09
2 Deli / Bodega 0.08
3 Bakery 0.05
4 Bank 0.03
----Boerum Hill----
venue freq
0 Dance Studio 0.05
1 Coffee Shop 0.05
2 Bar 0.03
3 Spa 0.03
4 French Restaurant 0.03
----Clinton Hill----
venue freq
0 Italian Restaurant 0.05
1 Pizza Place 0.05
2 Mexican Restaurant 0.04
3 Indian Restaurant 0.03
4 Thai Restaurant 0.03
----College Point----
venue freq
0 Pizza Place 0.09
1 Deli / Bodega 0.09
2 Asian Restaurant 0.07
3 Bar 0.05
4 Latin American Restaurant 0.05
----East Elmhurst----
venue freq
0 Donut Shop 0.15
1 Rental Car Location 0.08
2 Ice Cream Shop 0.08
3 Supermarket 0.08
4 Gas Station 0.08
----Gowanus----
venue freq
0 Bar 0.08
1 Italian Restaurant 0.06
2 Furniture / Home Store 0.06
3 Coffee Shop 0.05
4 Gym / Fitness Center 0.05
----Greenpoint----
venue freq
0 Bar 0.08
1 Cocktail Bar 0.06
2 Coffee Shop 0.06
3 Pizza Place 0.06
4 Yoga Studio 0.03
----Hamilton Heights----
venue freq
0 Café 0.07
1 Mexican Restaurant 0.07
2 Pizza Place 0.07
3 Coffee Shop 0.05
4 Deli / Bodega 0.05
----Kingsbridge----
venue freq
0 Pizza Place 0.09
1 Deli / Bodega 0.06
2 Sandwich Place 0.06
3 Mexican Restaurant 0.04
4 Bakery 0.04
----Melrose----
venue freq
0 Pharmacy 0.15
1 Pizza Place 0.11
2 Discount Store 0.07
3 Supermarket 0.07
4 Donut Shop 0.04
----Pelham Gardens----
venue freq
0 Donut Shop 0.09
1 Pizza Place 0.09
2 Spanish Restaurant 0.09
3 Chinese Restaurant 0.09
4 Pharmacy 0.09
----Pleasant Plains----
venue freq
0 Pizza Place 0.15
1 Donut Shop 0.15
2 Discount Store 0.08
3 Bank 0.08
4 Ice Cream Shop 0.08
----Port Richmond----
venue freq
0 Pizza Place 0.29
1 Donut Shop 0.14
2 Bar 0.14
3 Bus Stop 0.14
4 Rental Car Location 0.14
----Steinway----
venue freq
0 Rental Car Location 0.13
1 Cosmetics Shop 0.09
2 Deli / Bodega 0.09
3 Donut Shop 0.04
4 Japanese Restaurant 0.04
----Tompkinsville----
venue freq
0 Café 0.08
1 Deli / Bodega 0.08
2 Thrift / Vintage Store 0.08
3 Grocery Store 0.04
4 Sri Lankan Restaurant 0.04
----Washington Heights----
venue freq
0 Café 0.06
1 Bakery 0.05
2 Grocery Store 0.03
3 Mobile Phone Shop 0.03
4 Sandwich Place 0.03
----Wingate----
venue freq
0 Fast Food Restaurant 0.14
1 Field 0.05
2 Juice Bar 0.05
3 Pharmacy 0.05
4 Café 0.05
----Woodlawn----
venue freq
0 Deli / Bodega 0.14
1 Pizza Place 0.10
2 Playground 0.07
3 Pub 0.07
4 Plaza 0.03
The Neighborhoods Steinway and East Elmhurst are the ones which has Rental Car Business in the top 5 venues. Let's group all the neighborhoods and find the Neighborhood which is similar to Steinway and East Elmhurst.
Now let's create the new dataframe and display the top 10 venues for each neighborhood.
newyork_venues_onehot = pd.get_dummies(Master_df[['Venue Category']], prefix="", prefix_sep="")
# add neighborhood column back to dataframe
newyork_venues_onehot['Neighborhood'] = Master_df['Neighborhood']
# move neighborhood column to the first column
fixed_columns = [newyork_venues_onehot.columns[-1]] + list(newyork_venues_onehot.columns[:-1])
newyork_venues_onehot = newyork_venues_onehot[fixed_columns]
print("One hot encoding for Mastet dataframe is done..........................................................................")
new_york_grouped = newyork_venues_onehot.groupby('Neighborhood').mean().reset_index()
print("dataframe is grouped based on Neighborhood column and mean function is applied to all columns..........................")
One hot encoding for Mastet dataframe is done.......................................................................... dataframe is grouped based on Neighborhood column and mean function is applied to all columns..........................
def return_most_common_venues(row, num_top_venues):
row_categories = row.iloc[1:]
row_categories_sorted = row_categories.sort_values(ascending=False)
return row_categories_sorted.index.values[0:num_top_venues]
indicators = ['st', 'nd', 'rd']
# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
try:
columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
except:
columns.append('{}th Most Common Venue'.format(ind+1))
# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = new_york_grouped['Neighborhood']
for ind in np.arange(new_york_grouped.shape[0]):
neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(new_york_grouped.iloc[ind, :], num_top_venues)
neighborhoods_venues_sorted.head()
| Neighborhood | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | |
|---|---|---|---|---|---|---|
| 0 | Allerton | Pizza Place | Supermarket | Chinese Restaurant | Pharmacy | Department Store |
| 1 | Annadale | Pizza Place | Bakery | Bagel Shop | Train Station | Sports Bar |
| 2 | Arden Heights | Pharmacy | Coffee Shop | Bus Stop | Pizza Place | Filipino Restaurant |
| 3 | Arlington | Bus Stop | Intersection | Caribbean Restaurant | Women's Store | Fish Market |
| 4 | Arrochar | Bus Stop | Deli / Bodega | Italian Restaurant | Pizza Place | Hotel |
neighborhoods_venues_sorted.shape[0]
300
# set number of clusters
kclusters = 5
new_york_grouped_clustering = new_york_grouped.drop('Neighborhood', 1)
# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(new_york_grouped_clustering)
# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]
array([0, 0, 0, 4, 2, 0, 0, 0, 0, 0])
# add clustering labels
neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)
newyork_merged = Master_df
# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood
newyork_merged = newyork_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')
newyork_merged.head() # check the last columns!
| Borough | Neighborhood | Neighborhood Latitude | Neighborhood Longitude | Venue | Venue Latitude | Venue Longitude | Venue Category | Cluster Labels | 1st Most Common Venue | 2nd Most Common Venue | 3rd Most Common Venue | 4th Most Common Venue | 5th Most Common Venue | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Bronx | Wakefield | 40.894705 | -73.847201 | Lollipops Gelato | 40.894123 | -73.845892 | Dessert Shop | 4 | Gas Station | Dessert Shop | Sandwich Place | Caribbean Restaurant | Ice Cream Shop |
| 1 | Bronx | Wakefield | 40.894705 | -73.847201 | Rite Aid | 40.896649 | -73.844846 | Pharmacy | 4 | Gas Station | Dessert Shop | Sandwich Place | Caribbean Restaurant | Ice Cream Shop |
| 2 | Bronx | Wakefield | 40.894705 | -73.847201 | Carvel Ice Cream | 40.890487 | -73.848568 | Ice Cream Shop | 4 | Gas Station | Dessert Shop | Sandwich Place | Caribbean Restaurant | Ice Cream Shop |
| 3 | Bronx | Wakefield | 40.894705 | -73.847201 | Cooler Runnings Jamaican Restaurant Inc | 40.898276 | -73.850381 | Caribbean Restaurant | 4 | Gas Station | Dessert Shop | Sandwich Place | Caribbean Restaurant | Ice Cream Shop |
| 4 | Bronx | Wakefield | 40.894705 | -73.847201 | Shell | 40.894187 | -73.845862 | Gas Station | 4 | Gas Station | Dessert Shop | Sandwich Place | Caribbean Restaurant | Ice Cream Shop |
Let's find out which cluster Steinway and East Elmhurst belong to.
Steinway_label = newyork_merged[newyork_merged['Neighborhood'] == 'Steinway']['Cluster Labels'].iloc[0]
East_Elmhurst_label = newyork_merged[newyork_merged['Neighborhood'] == 'East Elmhurst']['Cluster Labels'].iloc[0]
print('The Neighbourhood {} belong to cluster {}'.format("Steinway",Steinway_label))
print('The Neighbourhood {} belong to cluster {}'.format("East Elmhurst",East_Elmhurst_label))
The Neighbourhood Steinway belong to cluster 0 The Neighbourhood East Elmhurst belong to cluster 0
Let's take a look at those Neighborhoods which fall into Cluster 0.
cluster_zero = newyork_merged[newyork_merged['Cluster Labels'] == 0]
cluster_table = pd.pivot_table(cluster_zero, index = ['Neighborhood'])
cluster_table.head()
| Cluster Labels | Neighborhood Latitude | Neighborhood Longitude | Venue Latitude | Venue Longitude | |
|---|---|---|---|---|---|
| Neighborhood | |||||
| Allerton | 0 | 40.865788 | -73.859319 | 40.865057 | -73.859414 |
| Annadale | 0 | 40.538114 | -74.178549 | 40.541011 | -74.177195 |
| Arden Heights | 0 | 40.549286 | -74.185887 | 40.551278 | -74.185412 |
| Arverne | 0 | 40.589144 | -73.791992 | 40.590142 | -73.795322 |
| Astoria | 0 | 40.768509 | -73.915654 | 40.766627 | -73.916589 |
cluster_table['Neighborhood'] = cluster_table.index
cluster_table.columns
Index(['Cluster Labels', 'Neighborhood Latitude', 'Neighborhood Longitude',
'Venue Latitude', 'Venue Longitude', 'Neighborhood'],
dtype='object')
# create map
map_clusters = folium.Map(location=[40.7127281, -74.0060152], zoom_start=15)
# add markers to the map
for index,row in cluster_table.iterrows():
label = folium.Popup(row['Neighborhood'] + ' Cluster ' + str(cluster), parse_html=True)
folium.CircleMarker(
[row["Neighborhood Latitude"], row["Neighborhood Longitude"]],
radius=2,
popup=label,
fill_opacity=0.7).add_to(map_clusters)
newyorkArr = cluster_table[['Neighborhood Latitude', 'Neighborhood Longitude']].as_matrix()
# plot heatmap
map_clusters.add_children(plugins.HeatMap(newyorkArr, radius=15))
map_clusters
C:\Users\Welcome\Anaconda3\lib\site-packages\ipykernel_launcher.py:20: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead. C:\Users\Welcome\Anaconda3\lib\site-packages\ipykernel_launcher.py:23: FutureWarning: Method `add_children` is deprecated. Please use `add_child` instead.
Conclusion: The Neighborhoods that are suggested are as shown in the above heat map. The Businesses can be started in any of the above locations.